Systems and methods for providing training opportunities based on data collected from monitoring a physiological parameter of persons engaged in physical activity

ABSTRACT

The present disclosure provides systems and methods for providing training opportunities based on data collected from monitoring a physiological parameter of persons engaged in physical activity. The physical activity can be a sporting activity, such as a contact sport (e.g., football, hockey, lacrosse) or a recreational activity or sport (e.g., biking, hiking, skiing, snowboarding, motorsports). The system is configured with select components that perform a method of (i) recording data related to a physiological parameter of a person engaged in a physical activity (e.g., an impact received by a player engaged in a contact sport), (ii) analyzing the recorded data related to the physiological parameter while the person is engaged in a physical activity (e.g., is the received impact greater than a predetermined threshold), and (iii) providing post-physical activity analysis of the recorded data to make suggested changes in how the person engages in the physical activity.

CROSS-REFERENCE TO OTHER APPLICATIONS AND PAPERS

This Application claims the benefit of U.S. Provisional PatentApplication Ser. No. 62/778,559, entitled “Systems And Methods ForProviding Training Opportunities Based On Data Collected From MonitoringA Physiological Parameter Of Persons Engaged In Physical Activity,”filed on Dec. 12, 2018, all of these applications which are incorporatedherein by reference and made a part hereof.

U.S. Pat. No. 10,105,076 entitled “Systems And Methods For Monitoring APhysiological Parameter Of Persons Engaged In Physical Activity,” filedon Sep. 4, 2012, U.S. Provisional Patent Application Ser. No. 61/530,282entitled “System & Method For Monitoring A Physiological Parameter OfPersons Engaged In Physical Activity,” filed on Sep. 1, 2011, and U.S.Provisional Patent Application Ser. No. 61/533,038 entitled “System &Method For Monitoring A Physiological Parameter Of Persons Engaged InPhysical Activity,” filed on Sep. 9, 2011, the disclosure of which ishereby incorporated by reference in its entirety for all purposes.

U.S. Pat. No. 9,622,661 entitled “Impact Monitoring System For PlayersEngaged In A Sporting Activity,” filed on Oct. 7, 2013 and U.S.Provisional Patent Application Ser. No. 60/239,379 entitled“Multi-Directional Head Acceleration System,” filed on Oct. 11, 2000,the disclosure of which is hereby incorporated by reference in itsentirety for all purposes.

U.S. Pat. No. 8,797,165 entitled “System For Monitoring A PhysiologicalParameter Of Players Engaged In A Sporting Activity,” filed on Sep. 13,2005 and U.S. Provisional Patent Application Ser. No. 60/609,555entitled “System For Measuring And Monitoring Acceleration Of A BodyPart,” filed on Sep. 13, 2004, the disclosure of which is herebyincorporated by reference in its entirety for all purposes.

U.S. Pat. No. 8,548,768 entitled “System And Method For Evaluating AndProviding Treatment To Sports Participants,” filed on Jan. 9, 2005, andU.S. Provisional Patent Application Ser. No. 60/642,240 entitled “SystemAnd Method For Evaluating And Providing Treatment To SportsParticipants,” filed on Jan. 7, 2005, the disclosure of these are herebyincorporated by reference in their entirety for all purposes.

U.S. patent application Ser. No. 16/691,436 entitled “Football Helmetwith Components Additively Manufactured to Manage Impact Forces,” filedon Nov. 21, 2019, U.S. Design patent application Ser. No. 29/671,111,entitled “Internal Energy attenuation assembly of a Protective SportsHelmet,” filed on Nov. 22, 2018 and U.S. Provisional Patent ApplicationSer. No. 62/770,453, entitled “Football Helmet With ComponentsAdditively Manufactured To Optimize The Management Of Energy From ImpactForces,” filed on Nov. 21, 2018, the disclosure of these are herebyincorporated by reference in their entirety for all purposes.

U.S. patent application Ser. No. 16/543,371 entitled “System And MethodFor Designing And Manufacturing A Protective Helmet Tailored To ASelected Group Of Helmet Wearers,” filed on Aug. 16, 2019 and U.S.Provisional Patent Application Ser. No. 62/719,130 entitled “System andMethods for Designing and Manufacturing a Protective Sports Helmet Basedon Statistical Analysis of Player Head Shapes,” filed on Aug. 16, 2018,the disclosure of these are hereby incorporated by reference in theirentirety for all purposes.

U.S. patent application Ser. No. 15/655,490 entitled “System And MethodsFor Designing And Manufacturing A Bespoke Protective Sports Helmet,”filed on Jul. 20, 2017, U.S. Pat. No. 10,159,296 entitled “System andMethod for Custom Forming a Protective Helmet for a Customers Head,”filed on Jan. 15, 2014, U.S. Pat. No. 9,314,063 entitled “FootballHelmet with Impact Attenuation System,” filed on Feb. 12, 2014, U.S.Design Pat. D764,716 entitled “Football Helmet,” filed on Feb. 2, 2012,U.S. Pat. No. 9,289,024 entitled “Protective Sports Helmet,” filed onMay 2, 2011, and U.S. Design Pat. D603,099 entitled “Sports Helmet,”filed on Oct. 27, 2009, the disclosure of these are hereby incorporatedby reference in their entirety for all purposes.

Crisco J J, et. al. An Algorithm for Estimating Acceleration Magnitudeand Impact Location Using Multiple Nonorthogonal Single-AxisAccelerometers. J Bio Mech Eng. 2004; 126(1), Duma S M, et. al. Analysisof Real-time Head Accelerations in Collegiate Football Players. Clin JSport Med. 2005; 15(1):3-8, Brolinson, P. G., et al. “Analysis of LinearHead Accelerations from Collegiate Football Impacts.” Current SportsMedicine Reports, vol. 5, no. 1, 2006, pp. 23-28, Greenwald R M, et.,al. Head impact severity measures for evaluating mild traumatic braininjury risk exposure. Neurosurgery. 2008; 62(4):789-798, J. J. Crisco,et., al. Frequency and location of head impact exposures in individualcollegiate football players. J. Athl. Train., 45 (2010), pp. 549-559,and Rowson, S., et., al. A six degree of freedom head accelerationmeasurement device for use in football. J. Appl. Biomech. 27:8-14, 2011,the disclosure of which is hereby incorporated by reference in itsentirety for all purposes.

TECHNICAL FIELD

This disclosure relates to a system and method for: (i) recording datarelated to a physiological parameter of a person engaged in a physicalactivity (e.g., an impact experienced by a player engaged in a contactsport), (ii) analyzing the recorded data related to the physiologicalparameter while the person is engaged in a physical activity (e.g., isthe experienced impact greater than a predetermined threshold), and(iii) providing post-physical activity analysis of the recorded data tomake suggested changes in how the person engages in the physicalactivity.

BACKGROUND

There is a concern in various contact sports, such as football, lacrosseand hockey, of brain injury due to impacts to the head of an individualengaged in playing the contact sport. During such physical activity, thehead of the individual is often subjected to contact which results in animpact to the skull and brain of the individual, as well as the movementof the head or body part itself.

While considerable research has been under taken in the scientificcommunity, a fair amount of information regarding the response of thebrain to head accelerations in the linear and rotational directions andeven less about the correspondence between specific impact forces andinjury, particularly with respect to injuries caused by repeatedexposure to impact forces of a lower level than those that result in acatastrophic injury or fatality. A considerable amount of what was knownis derived from animal studies, studies of cadavers under specificdirectional and predictable forces (i.e. a head-on collision test), fromcrash dummies, from human volunteers in well-defined but limited impactexposures or from other simplistic mechanical models. The conventionalapplication of known forces and/or measurement of forces applied toanimals, cadavers, crash dummies, and human volunteers limit ourknowledge of a relationship between forces applied to a living humanhead and any resultant severe brain injury. These prior studies alsohave limited value as they typically relate to research in non-contactsports settings, such as automobile safety area.

The concern for sports-related injuries, particularly to the head, ishigher than ever. The Center for Disease Control and Preventionestimates that the incidence of sports-related mild traumatic braininjury (MTBI) approaches 300,000 annually in the United States.Approximately one-third of these injuries occur in football, with MTBIbeing a major source of lost playing time. Head injuries accounted for13.3% of all football injuries to boys and 4.4% of all soccer injuriesto both boys and girls in a large study of high school sports injuries.Approximately 62,800 MTBI cases occur annually among high school varsityathletes, with football accounting for about 63% of cases. It has beenreported that concussions in hockey affect 10% of the athletes andmakeup 12%-14% of all injuries.

For example, a typical range of 4-6 concussions per year in a footballteam of 90 players (7%), and 6 per year from a hockey team with 28players (21%) is not uncommon. In rugby, concussions can affect as manyas 40% of players on a team each year. Concussions, particularly whenrepeated multiple times, significantly threaten the long-term health ofthe athlete. The health care costs associated with MTBI in sports areestimated to be in the hundreds of millions of dollars annually. TheNational Center for Injury Prevention and Control considerssports-related traumatic brain injury (mild and severe) an importantpublic health problem because of the high incidence of these injuries,the relative youth of those being injured with possible long termdisability, and the danger of cumulative effects from repeat incidences.

Athletes who suffer head impacts during a practice or game situationoften find it difficult to assess the severity of the impact even withthe assistance of coaches and trainers. In an attempt to assess theseverity of the impact, devices have been developed that attempt torecord the acceleration/deceleration of a player's head when the playerexperiences an impact. Examples of such devices are discussed withinpatents (e.g., U.S. Pat Nos. 10,105,076, 9,622,661, 8,797,165, and8,548,768) that are assigned to the current assignee of thisapplication. While these devices may be able to detect and recordacceleration/deceleration of a player's head when the player experiencesan impact, these devices cannot determine if the impacts experienced areuncommon for the player or if the number/frequency of impacts that theplayer is experiencing are uncommon for the player's level and/orposition. For example, a player may not be using proper tackling form(e.g., leading with the crown of his helmet) or after returning from aninjury, the player may improperly alter his form to protect the part ofthe player's body that was previously injured. In a further example, theplayer may be experiencing more impacts during a day, week, or season incomparison to players of similar playing levels and/or positions, whichmay inform a coach or member of the coaching staff that the player needsadditional instruction on tackling techniques and/or additional breaksto help ensure that they do not maintain a high head impact exposure(“HIE”) load.

Based on the above, there is a demand for a physiological measuring andreporting system that is designed for post-activity analysis, whichtakes into account the player's level and/or position in determining ifthe player is experiencing impacts that are uncommon for the player'slevel and/or position. Additionally, there is also a demand that theforegoing system not only takes into account the player's level and/orposition, but uses algorithms that self-update the thresholds that areutilized to determine if the impacts that the player is experiencing areuncommon for the player's level and/or position. Further, there is ademand for a graphical user interface (“GUI”) that is shown on adisplay, wherein the GUI includes summary screens and other intuitivescreens to increase the usability of the system and efficiently presentinformation to the authorized user (e.g., coach, trainer, equipmentmanager, other member of the coaching staff, administrator, parent ofthe player, or the player, or other similar individuals).

This disclosure addresses shortcomings discussed above and otherproblems and provides advantages and aspects not provided by the priorart of this type. A full discussion of the features and advantages ofthe present disclosure is deferred to the following detaileddescription, which proceeds with reference to the accompanying drawings.

SUMMARY

The present disclosure provides a system for monitoring at least onephysiological parameter of multiple players engaged in a contact sport.The system includes a plurality of monitoring units, each monitoringunit being associated with a specific or target player and having asensor assembly that actively monitors at least one physiologicalparameter of the target player while engaged in the contact sport todetermine a physiological parameter value. The monitoring unitselectively generates a first alert when the physiological parametervalue exceeds a first predetermined threshold based upon a singleincidence of the physiological parameter and a second alert when thephysiological parameter value exceeds a second predetermined thresholdbased upon cumulative incidences of the physiological parameter. Thesystem also includes a portable alert unit that receives the first alertand second alert transmitted from a particular monitoring unit anddisplays information relating to the particular alert to a user of thesystem.

The system also provides training opportunities geared for theauthorized user, typically the coaching staff and/or athletic trainers.The training opportunities are based on comparing an individual player'sdata, a subset of the team's data, or the team's data against similarcollections of data on different scales (e.g., team scale, local scale,regional scale, national scale, or nation-wide/worldwide scale).Specifically, training opportunities for an individual player may bebased on comparing a player's recent physiological parameter dataagainst various collections of historical physiological parameter datafor other similar players (e.g., players that play at a similar playinglevel and/or position). For example, various collections of data mayinclude: (A) player's own historical data, (B) team's historical data,(C) historical local player, position, unit or team data, (D) historicalregional player, position, unit or team data, or (E) historical nationalplayer, position, unit or team data. Other training opportunities for anindividual player may also be based on comparing a player's recentphysiological parameter data against various collections of recentphysiological parameter data for other similar players. For example,various collections of data may include: (A) team's recent data, (B)recent local player, position, unit or team data, (C) recent regionalplayer, position, unit or team data, or (D) recent national player,position, unit, or team data. Alternatively, the system may adjust thealgorithms that generate the training opportunities based upon thatplayer's historical data, not solely historical data of other similarplayers.

Additionally, training opportunities for all players that play aspecific position on a team (e.g., all players within a team thatprimary play one position, such as lineman or running backs) and theirhistorical data may be based on comparing a team's recent positionalphysiological parameter data against various collections of historicalphysiological parameter data for that specific position or other similarpositions. For example, collections of data may include: (A) position'sown historical data, (B) team's historical data, (C) historical localposition, unit or team data, (D) historical regional position unit orteam data, or (E) historical national position, unit or team data. Othertraining opportunities for all players that play a specific position ona team may also be based on comparing a team's positional recentphysiological parameter data against various collections of recentphysiological parameter data for other similar players. For example,various collections of data may include: (A) team's recent data, (B)recent local position, unit or team data, (C) recent regional positionunit or team data, or (D) recent national position, unit, or team data.

Further, training opportunities for a unit's (e.g., all players within ateam that primarily play in one unit, such as the “offense,” “defense,”kickoff,” “field goal” unit) historical data may be based on comparing aunit's recent physiological parameter data against various collectionsof historical physiological parameter data for specific units or othersimilar units. For example, collections of data may include: (A) unit'sown historical data, (B) team's historical data, (C) historical localunit or team data, (D) historical regional unit or team data, or (E)historical national unit or team data. Other training opportunities fora unit may also be based on comparing a unit's recent physiologicalparameter data against various collections of recent physiologicalparameter data for other similar units. For example, such variouscollections of similar data may include: (A) team's recent data, (B)recent local unit or team data, (C) recent regional unit or team data,or (D) recent national unit or team data.

Moreover, training opportunities for a team's historical data may bebased on comparing a team's recent physiological parameter data againstvarious collections of historical physiological parameter data for othersimilar teams. For example, various collections of similar physiologicaldata may include: (A) team's own historical data, (B) historical localteam data, (C) historical regional team data, or (D) historical nationalteam data. Other training opportunities for a team may also be based oncomparing a team's recent physiological parameter data against variouscollections of recent physiological parameter data for other similarteams. For example, various collections of similar physiological datamay include: (A) recent local team data, (B) recent regional team data,or (C) recent national team.

Also, it should be understood that other training opportunities arecontemplated by this disclosure.

Additional advantages and novel features will be set forth in part inthe description which follows, and in part will become apparent to thoseskilled in the art upon examination of the following and theaccompanying drawings or may be learned by production or operation ofthe examples. The advantages of the present teachings may be realizedand attained by practice or use of various aspects of the methodologies,instrumentalities and combinations set forth in the detailed examplesdiscussed below.

BRIEF DESCRIPTION OF THE DRAWINGS

This patent or application file contains at least one drawing executedin color. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the Office upon request and paymentof the necessary fee. The drawing figures depict one or moreimplementations in accord with the present teachings, by way of exampleonly, not by way of limitation. In the figures, like reference numeralsrefer to the same or similar elements.

FIG. 1 illustrates a first exemplary multi-functional system showing:(i) helmets that each have an in-helmet unit (IHU) installed therein,(ii) a receiving device, which is an alert unit, (iii) a remoteterminal, (iv) a team database, and (v) a national database;

FIG. 1A illustrates a first embodiment of an alert unit with a firstembodiment of a graphical interface;

FIG. 1B illustrates a second embodiment of an alert unit with a secondembodiment of a graphical interface;

FIG. 2 illustrates a second exemplary system showing: (i) helmets thateach have an IHU installed therein, (ii) a receiving device, which is analert unit, (iii) a remote terminal, and (iv) a combined team databaseand national database;

FIG. 3 illustrates a third exemplary system showing: (i) helmets thateach have an IHU installed therein, (ii) a receiving device, which is acombination of an alert unit, remote terminal, and a team database, and(iii) a national database;

FIG. 4 illustrates a fourth exemplary system showing: (i) helmets thateach have an IHU installed therein, (ii) a receiving device, which is acontroller, (iii) an alerting unit, (iv) a team database, (v) a nationaldatabase, and (vi) a remote terminal;

FIG. 5 illustrates a fifth exemplary system showing: (i) helmets thateach have an IHU installed therein, (ii) receiving device, whichincludes both a network element and a remote terminal, (iii) an alertingunit, (iv) a team database, (v) a national database;

FIG. 6 illustrates a sixth exemplary system showing: (i) helmets thateach have an IHU installed therein, (ii) receiving device, which is anetwork element, (iii) team database, (iv) a national database, and (v)a combination of an alerting unit and a remote terminal.

FIG. 7A illustrates an exemplary IHU that fits within the helmet,wherein the IHU is in a substantially flat, pre-installed,configuration;

FIG. 7B is a perspective view of the exemplary IHU from FIG. 7A, whereinthe IHU is in a folded configuration that minims an installedconfiguration;

FIG. 8A side view of the exemplary IHU from FIG. 7A, wherein the IHU isinserted within an overliner that is installed against an inner surfaceof an energy attenuation assembly of the helmet;

FIG. 8B is a rear view of the exemplary IHU from FIG. 7A, wherein theIHU is inserted within the helmet's overliner and the overliner ispositioned within the energy attenuation assembly;

FIG. 9A is a partial top view of the helmet;

FIG. 9B is a first cross-sectional view of the helmet taken along the9B-9B line shown in FIG. 9A, wherein the energy attenuation assembly isconfigured to receive an extent of the IHU when it is installed withinthe helmet;

FIG. 9C is a second cross-sectional view of the helmet taken along the9C-9C line shown in FIG. 9A, wherein the energy attenuation assembly isconfigured to receive an extent of the IHU when it is installed withinthe helmet;

FIG. 10 illustrates a 3D model of the helmet with the IHU installedtherein and a faceguard attached to the helmet;

FIG. 11 is a schematic of the IHU shown in FIG. 10;

FIG. 12 is an exemplary flowchart showing the data flow within thehelmet after an impact has been detected by the IHU;

FIG. 13A is a chart showing categories of potential trainingopportunities;

FIG. 13B is an exemplary chart showing additional categories ofpotential training opportunities for a player;

FIG. 13C is an exemplary chart showing additional categories ofpotential training opportunities for a position;

FIG. 13D is a chart showing additional categories of potential trainingopportunities for a unit;

FIG. 13E is a chart showing additional categories of potential trainingopportunities for a unit;

FIG. 14 is a flowchart showing how training opportunities 1, 2, 9, 10,17, and 18 are determined;

FIG. 15 is a flowchart showing how training opportunities 3 and 14 aredetermined;

FIG. 16 is a flowchart showing how training opportunities 4, 5, 11, 15,and 19 are determined;

FIG. 17 is a flowchart showing how training opportunities 6, 7, 12, 16,and 20 are determined;

FIG. 18 is a flowchart showing how training opportunities 8, 13, and 21are determined;

FIG. 19 is a flowchart showing how the self-updating thresholds areupdated;

FIG. 20 illustrates a reference image of a player's head split into fivedifferent regions (i.e., front, back, left, right, top);

FIG. 21 is a schematic showing how to impact matrixes can be addedtogether;

FIG. 22 is a chart showing the calculations that are performed byalgorithm 508 for an exemplary player;

FIG. 23 is a chart showing the calculations that are performed byalgorithm 512 for an exemplary player;

FIG. 24 is a chart showing the calculations that are performed byalgorithm 516 for an exemplary player;

FIG. 25A is a player's impact matrix;

FIG. 25B is a national average impact matrix;

FIG. 25C is an algorithm used to determine the estimated Chi value forthe comparison between the player's impact matrix against the nationalaverage impact matrix;

FIG. 25D is an algorithm used to compare the estimated Chi value againstthe actual Chi value determined from the table shown in FIG. 25E;

FIG. 25E is a table that is used to determine the actual Chi value usedin the algorithm shown in FIG. 25D;

FIG. 26 shows a landing page of the GUI, which can be shown on thedisplay contained within the multi-faceted system;

FIG. 27 illustrates a first screen contained within the coaching tool ofthe GUI;

FIG. 28A illustrates a first section of the first screen of the coachingtool of the GUI, which includes a month view of a practice planner;

FIG. 28B illustrates a second section of the first screen of thecoaching tool of the GUI, which includes a month view of an impact trendchart;

FIG. 28C illustrates a third section of the first screen of the coachingtool of the GUI, which includes a month view of an alert chart;

FIG. 28D illustrates a fourth section of the first screen of thecoaching tool of the GUI, which includes a month view of a trainingopportunities chart;

FIG. 29 illustrates a second screen contained within the coaching toolof the GUI;

FIG. 30A illustrates a first section of the second screen of thecoaching tool of the GUI, which includes a day view of a practiceplanner;

FIG. 30B illustrates a screen for editing practice plans within the GUI;

FIG. 30C illustrates a second section of the second screen of thecoaching tool of the GUI, which includes a day view of an impact trendchart for a team along with an alert chart;

FIG. 31 illustrates a replacement second section of the second screen ofthe coaching tool of the GUI, which includes a day view of an impacttrend chart for a unit of the team (i.e., offense);

FIG. 32 illustrates a replacement second section of the second screen ofthe coaching tool of the GUI, which includes a day view of an impacttrend chart for a selected player position (i.e., running back);

FIG. 33 illustrates a replacement second section of the second screen ofthe coaching tool of the GUI, which includes a day view of the impacttrend chart for a selected player (i.e., Conrad Collins);

FIG. 34 illustrates a third section of the second screen of the coachingtool of the GUI, which includes a day view of an impact trend chart fora team along with the training opportunity chart;

FIG. 35 illustrates a third screen of the coaching tool of the GUI,which includes a first example of a player report for a predefinedamount of time (e.g., 7 days) and showing a training opportunity basedupon impact location;

FIG. 36 illustrates a first screen contained within the impact analyticstool of the GUI, which includes an HIE load chart and the alert chart;

FIG. 37 illustrate a first section of the first screen contained withinthe impact analytics tool of the GUI, which includes an HIE load chartfor a selected unit (i.e., offense) of team;

FIG. 38 illustrate a replacement first section of the first screencontained within the impact analytics tool of the GUI, which includes aHIT_(sp) load chart for a selected unit (i.e., offense) of team;

FIG. 39 illustrate a replacement first section of the first screencontained within the impact analytics tool of the GUI, which includes anHIE location chart for a selected unit (i.e., offense) of the team;

FIG. 40 illustrate the first section of the first screen containedwithin the impact analytics tool of the GUI, which includes the HIE loadchart for a selected unit (i.e., offense) of the team;

FIG. 41 illustrate a replacement first section of the first screencontained within the impact analytics tool of the GUI, which includes aHIT_(Sp) load chart for a selected unit (i.e., defense) of the team;

FIG. 42 illustrate a replacement first section of the first screencontained within the impact analytics tool of the GUI, which includes anHIE location chart for a selected unit (i.e., offense) of the team;

FIG. 43 illustrate a replacement first section of the first screencontained within the impact analytics tool of the GUI, which includes anHIE load chart for a selected player position (i.e., linebacker);

FIG. 44 illustrate a replacement first section of the first screencontained within the impact analytics tool of the GUI, which includes anHIE load chart for a first selected player (i.e., Vin Malloy);

FIG. 45 illustrate a replacement first section of the first screencontained within the impact analytics tool of the GUI, which includes aHIT_(Sp) load chart for the first selected player (i.e., Vin Malloy);

FIG. 46 illustrate a replacement first section of the first screencontained within the impact analytics tool of the GUI, which includes anHIE location chart for the first selected player (i.e., Vin Malloy);

FIG. 47 illustrate a replacement first section of the first screencontained within the impact analytics tool of the GUI, which includes anHIE location chart for a second selected player (i.e., Rex Bruce);

FIG. 48 illustrate a replacement first section of the first screencontained within the impact analytics tool of the GUI, which includes anHIE location chart for a third selected player (i.e., Joey Caffrey);

FIG. 49 illustrates a second screen contained within the impactanalytics tool of the GUI, which includes an HIE load chart and thetraining opportunity chart;

FIG. 50 illustrates a third screen of the impact analytics tool of theGUI, which includes a second example of a player report for a predefinedamount of time (e.g., 7 days) and showing a training opportunity basedupon impact intensity;

FIG. 51 illustrates a fourth screen of the impact analytics tool of theGUI, which includes a third example of a player report for a predefinedamount of time (e.g., 7 days) showing a training opportunity based uponimpact load;

FIG. 52 illustrates a fifth screen of the impact analytics tool of theGUI, which includes a fourth example of a player report for a predefinedamount of time (e.g., 7 days) and showing a training opportunity basedupon impact volume and load;

FIG. 53 illustrates a sixth screen of the impact analytics tool of theGUI, which includes a fifth example of a player report for a predefinedamount of time (e.g., 7 days) and showing no training opportunity beingtriggered;

FIG. 54 illustrates a first screen of the team report tool of the GUI,which shows data that was recorded of a reporting period;

FIG. 55A illustrates a first section of the first screen of the teamreport tool of the GUI;

FIG. 55B illustrates a second section of the first screen of the teamreport tool of the GUI;

FIG. 56 illustrates a first weekly report for a team that is sent to anauthorized user;

FIG. 57 illustrates a first daily report for a team that is sent to anauthorized user;

FIG. 58 illustrates a first screen of the roster tool of the GUI, whichincludes players that are on the team's roster;

FIG. 59 illustrates a first screen of the roster tool of the GUI, whichincludes players that are on the team's roster;

FIG. 60 illustrates a first screen of the roster tool of the GUI, whichincludes players that are on the team's roster;

FIG. 58 illustrates a first screen of the roster tool of the GUI, whichincludes players that are on the team's roster;

FIG. 59 illustrates a first screen of the administrator tool of the GUI;

FIG. 60 illustrates a second screen of the administrator tool of theGUI;

FIG. 61A illustrates a first section of a third screen of theadministrator tool of the GUI;

FIG. 61B illustrates a second section of a third screen of theadministrator tool of the GUI; and

FIG. 61C illustrates a third section of a third screen of theadministrator tool of the GUI.

DETAILED DESCRIPTION 1. Introduction/Overview of the Inventive System

In the following detailed description, numerous specific details are setforth by way of examples in order to provide a thorough understanding ofthe relevant teachings. However, it should be apparent to those skilledin the art that the present teachings may be practiced without suchdetails. In other instances, well-known methods, procedures, components,and/or circuitry have been described at a relatively high-level, withoutdetail, in order to avoid unnecessarily obscuring aspects of the presentteachings.

While this disclosure includes a number of embodiments in many differentforms, there is shown in the drawings and will herein be described indetail particular embodiments with the understanding that the presentdisclosure is to be considered as an exemplification of the principlesof the disclosed methods and systems, and is not intended to limit thebroad aspects of the disclosed concepts to the embodiments illustrated.As will be realized, the disclosed methods and systems are capable ofother and different configurations and several details are capable ofbeing modified all without departing from the scope of the disclosedmethods and systems. For example, one or more of the followingembodiments, in part or whole, may be combined consistent with thedisclosed methods and systems. As such, one or more steps from the flowcharts or components in the Figures may be selectively omitted and/orcombined consistent with the disclosed methods and systems. Accordingly,the drawings, flow charts and detailed descriptions are to be regardedas illustrative in nature, not restrictive or limiting.

FIGS. 1-6 illustrate exemplary embodiments of a multi-functional system10 that: (i) records data related to a physiological parameter of aperson engaged in a physical activity (e.g., an impact experienced by aplayer engaged in a contact sport), (ii) analyzes the recorded datarelated to the physiological parameter, while the person is engaged in aphysical activity (e.g., is the experienced impact greater than athreshold), (iii) transmits the recorded data related to thephysiological parameter, while the person is engaged in a physicalactivity, to an external device (e.g., if experienced impact is greaterthan a threshold, then part of the system sends an alert to an alertunit), and (iv) provides post-physical activity analysis of the recordeddata to make suggested changes in how the person engages in the physicalactivity.

To provide post-physical activity analysis, the multi-functional system10 utilizes a complex collection of algorithms to analyze data relatedto at least one physiological parameter (e.g., pressure) for a selectedplayer or group of players to inform the authorized user that theselected player or group of players have experienced physiologicalparameters that are uncommon or atypical for the selected player orgroup. This complex collection of algorithms provides an unconventionalsolution to the problem of trying to understand what the playerexperiences during the activity. This unconventional solution is rootedin technology and provides information that was not available inconventional systems. This unconventional solution also represents animprovement in the subject technical field otherwise unrealized byconventional systems. Specifically, unlike conventional systems, themulti-functional system 10 determines if the player or group of playersexperiences, for example: (i) more alertable impacts then otherplayer/groups, (ii) more high magnitude impacts then otherplayer/groups, (iii) more impacts then the player or group of playershas experienced in a past interval of time, (iv) more high magnitudeimpacts then the player or group of players has experienced in a pastinterval of time, (v) impacts in uncommon locations of a body part(e.g., irregular locations of the head) or patterns in comparison toother player/groups, (vi) impacts in uncommon locations of a body part(e.g., irregular locations of the head) or patterns in comparison to theimpacts that the player or group of players has experienced in a pastinterval of time, and (vii) other determinations that are discussedbelow.

After system 10 makes these determinations about the player or group ofplayers, the system 10 displays the data using a GUI on a display 28 ain a unique and easy to understand format. Conventional devices couldnot provide this solution for at least the following reasons: (i) thesignificant processing power required for the in-helmet units, (ii) theconsiderable data storage requirements for the in-helmet units, (iii) alarge enough pool of physiological parameter data to provide accuratethresholds for the algorithms, (iv) algorithms that allow for thethresholds to be self-updated in light of additional data that is addedto the pool of physiological parameter data, (v) other hardware andsoftware features that are discussed below, or (vi) other reasons thatare known to one of skill in the art based on the disclosure herein.

The complex collection of algorithms are operational linked and tied tothe multi-functional system 10, which ensures that the disclosedalgorithms cannot preempt all uses of these algorithms beyond the system10. Also, as detailed below, these algorithms are complicated and cannotbe performed using a pen and paper or within the human mind. Inaddition, the GUI displays the results of the execution of these complexalgorithms in a manner that is easily understandable by a human user,sometimes views such results on a small or handheld screen 28 a,improves operation of computing devices. Additionally, translation ofoutcomes from these complex algorithms through the GUI onto imagesdisplayed for a user, improves comprehension of considerable quantitiesof highly processed data. For example, an exemplary algorithm from thiscomplex collection of algorithms requires: taking inputs from multiplesensors, selecting some data provided by the sensors, ignoring some ofthe data that was provided by the sensors, performing multiplecalculations on a selected subset of the data, combining the data fromthese multiple calculations and then outputting that data within a shortamount of time (e.g., preferably less than a minute), all for multiplemembers on a team.

The exemplary algorithm cannot be performed with a pen and paper orwithin the human mind because the algorithm requires analyzing millionsof data points to find similarities between individuals, groupingindividuals that have similarities together, determining thephysiological parameters that these similar individuals experience,determining the level of the physiological parameter that a similarindividual must experience to place an individual above 95% of thesesimilar individuals, obtaining additional physiological parameter dataabout the similar individuals, making a new determination about thelevel of the physiological parameter that a similar individual mustexperience to place above 95% of the similar individuals in light of thenewly obtained data, comparing physiological parameter data from similarindividuals against this new threshold, providing the results of thisanalysis to the authorized user, and then repeating the above steps overa relatively short time period (e.g., every day) and for hundreds ofdifferent groups of players. Additional reasons why this complexcollection of algorithms cannot be performed with a pen and paper orwithin the human mind will be obvious to one of skill in the art basedon the below disclosure.

Additionally, multi-functional system 10 provides multiple improvementsover conventional systems, including improving the efficiency ofmonitoring players to identify coaching and training opportunities viathe GUI that is displayed on a screen 28 a. The authorized user can thenutilize the information provided by the GUI to proactively identify,coach and adjust player behavior, group behavior, or team behaviorthrough new/different training techniques and practice plans. Forexample, in the context where the physiological parameter is pressureexerted on the player's head due to a helmet impact, themulti-functional system 10 learns the type of impacts a playerexperiences and identifies if the player deviates from these impacttypes over time. Also, the system 10 can determine if the impactsexperienced by the player deviate from other similarly situated players.Deviations indicate to the users of the system 10 that: (i) new ordifferent drills should be utilized or (ii) additional coaching within aparticular drill should be utilized to train the player in order toalter the number, magnitude, or type of impacts the player isexperiencing or may experience during future play of the contact sport.

Additionally, the system 10 learns the type of impacts a subset ofplayers of the team experiences and identifies if the subset of the teamdeviates from these types of impacts over time. Also, the system 10 candetermine if the impacts experienced by the player subset deviate fromother similar subsets on that team or other teams. Deviations indicateto the users of the system 10 that new or different drills could beutilized to train the player subset in order to alter the number,magnitude, or type of impacts the player subset is experiencing or mayexperience during future play of the contact sport.

Further, system 10 learns the type of impacts an entire team experiencesand identifies if the team deviates from these types of impacts overtime. Also, the system 10 can determine if the impacts experienced bythe team deviate from similar teams. Deviations indicate to the users ofthe system 10 that new or different drills could be utilized to trainthe team in order to alter the number, magnitude, or type of impacts theteam is experiencing or may experience during future play of the contactsport. Moreover, system 10 allows multiple people to collaborate,locally or remotely, about the use of these training techniques andpractice plans. Also, the system 10 allows for the tracking of aplayer's history (e.g., impacts, sizes, medical histories, equipment,etc.) and other relevant information to aid in the monitoring andtraining of the player.

The present disclosure, as will be discussed in detail below, is capableof monitoring and analyzing data gathered from any body part of anindividual but has particular application in monitoring the human head.For example, system 10 could be employed within protective equipmentother than helmets to monitor a player's shins, knees, hips, chest,shoulders, elbows, or wrists. Therefore, any reference to a body part isunderstood to encompass the head and any reference to the head alone isintended to include applicability to any body part. For ease ofdiscussion and illustration, discussion of the prior art and the presentdisclosure is directed to the human head, by way of example and is notintended to limit the scope of discussion to the human head.

Since most contact sports involve multi-player teams, the system 10actively monitors, records, analyzes, and transmits data related to theselected physiological parameter(s) for all players on the teamthroughout the course of play, including a game or practice session.System 10 is especially well suited for helmeted team sports whereplayers are susceptible to head impacts and injuries; for example,football, hockey, and lacrosse. The system 10 could also be applied tohelmets for a: baseball player, cyclist, polo player, equestrian rider,rock climber, auto racer, motorcycle rider, motocross racer, skier,skater, ice skater, snowboarder, snow skier and other snow or waterathletes, skydiver.

2. In-Helmet Unit (IHU)

FIGS. 1-6 and 10 illustrate exemplary configurations of the system 10.These exemplary configurations of the system 10 include a helmet 20 andan in-helmet unit (IHU) 22. The helmet 20 may include: (i) shell 20 a,(ii) faceguard 20 b, and (iii) internal energy attenuation assembly 20c. Exemplary shells 20 a, faceguards 20 b, and internal energyattenuation assemblies 20 c that may be used in connection with thesystem 10 disclosed herein are discussed in greater detail within U.S.patent application Ser. No. 16/691,436, U.S. Design patent applicationSer. No. 29/671,111, U.S. patent application Ser. No. 16/543,371, U.S.patent application Ser. No. 15/655,490, U.S. Pat. Nos. 10,159,296,9,314,063, 9,289,024, D764,716, D603,099, or any other helmet that hasbeen described above or is described within the materials that areincorporated by reference.

The IHU or monitoring unit 22 includes a sensor assembly 120 and acontrol module 130. The IHU 22 may be specifically designed andprogrammed to: (i) measure and record data related to a physiologicalparameter, (ii) analyze the recorded data using the algorithm shown inFIG. 12, and (iii) depending on the outcome of the algorithm shown inFIG. 12, transmit the recorded data to a receiving device 24 that isremote from the IHU 22. In one exemplary embodiment, this physiologicalparameter may be pressure on a player's head as a result of an impactthe player experienced while engaged in a contact sport, such asfootball, hockey or lacrosse. As discussed in greater detail below,physiological parameter data that is recorded in this exemplaryembodiment includes alert data and data contained within the impactmatrix. It should be understood that this exemplary embodiment shouldnot be construed as limiting and that the system disclosed herein can beapplied to other physiological parameters. Specifically, otherphysiological parameters that the system may record include:temperature, heart rate, blood pressure, balance, activity, or othersimilar physiological parameters.

In an alternative embodiment, the IHU 22 may be morphed to fit within amouthguard. It should be understood that this alternative embodiment isnot preferred because it requires placing a battery and other functionalcomponents within a user's mouth. Additionally, this configuration maycreate a device that exceeds the FCC transmission limits associated withdevices that are placed within a player's mouth when the mouth guardsends an alert to the receiving device 24. Nevertheless, it may bedesirable to be able to record data when a player is not wearing hishelmet 20 or is involved in an activity that does not utilize a helmet20. Thus, the designer may omit some of the components from the IHU 22in order to overcome some of the limitations associated with thismorphed embodiment. For example, the designer may create a mouth guardthat only records impact data and does not transmit alert data. Thismouth guard configuration can still be utilized by the system 10 becausethe impact data that it collects can be analyzed by the complexcollection of algorithms to provide training opportunity determinations.In further alternative embodiments and as described above, the IHU 22may be placed in other equipment that is worn on the player's body, suchas shins, knees, hips, chest, shoulders, elbows, and wrists. Forexample, the IHU 22 may be morphed to fit into a set of shoulder pads, ashin guard, jersey, pants, girdle, elbow pads, shoes, knee pads, gloves,jackets, boots, life vest, or other types of equipment that is worn bythe player.

Other components that may be included within the system 10 include: (i)the receiving device 24, which may be an alerting unit 26, (ii) a remoteterminal 28, (iii) a team database 32, and (iv) national database 38.Different configurations of these other components (e.g., thecombination of some of these components into a single combinationcomponent) are discussed in connection with FIGS. 1-6. At a high level,the receiving device 24 is a device that receives the alert data and/orthe impact data that is transmitted from the IHU 22. This receivingdevice 24 may be a simple relay or may be an internet enabled devicethat can display all or a subset of the alert data and/or impact data tothe authorized user. The remote terminal 28 may be an internet enableddevice that is configured to interact with the team database 32 and/ornational database 38 via the internet to obtain and display information(e.g., training opportunities) to the authorized user in the GUI. Thesetraining opportunities can then be utilized by the authorized user inorder to provide suggested changes in how the person engages in thephysical activity.

It should be understood that system 10 may include additional or fewercomponents. For example, system 10 may include additional database(s)that is external to the system and the databases 32, 38 therein. Thisexternal database(s) may store player/team data, such as: (i) videotapeof the games, scrimmages, or practices, (ii) activity levels of theplayer/team, or (iii) historical information about the player/team. Inanother embodiment, the system 10 may include the ability to connectother external systems in order to pull the data from these otherexternal systems into this system 10 for analysis. For example, in thisembodiment, the system 10 may be able to connect to a 3^(rd) partyexternal impact detection system in order to pull in the data that wasrecorded within that external impact detection system in order toanalyze the data using the algorithms contained within the system 10disclosed herein. In another example, these external systems may includea register's database at the school that includes the grades of theplayer. This information can be pulled into the system 10 disclosedherein to inform the authorized user of whether the player is authorizedto play that week. In a further example, these external systems mayinclude a weather database that can be used to record the weather duringgames or practice sessions. Also, this weather database could be used tosend an alert to the receiving device 24 to inform the authorized userthat the game or practice should be canceled or moved indoors. It shouldbe understood that these are just examples of other components that maybe added into the system 10 to provide the authorized user withadditional information about the player and these examples arenon-limiting.

a. Sensor Assembly Contained within the Helmet

FIGS. 7A-10 shows the IHU 22 in various configurations to enable theviewing of the layout of the sensor assembly 120, connector 132, and thecontrol module 130. As best shown in FIG. 7A, the sensor assembly 120includes five sensors 120 a, 120 b, 120 c, 120 d, and 120 e that eachprovides distinct electrical channels that measure at least onephysiological parameter of a player (e.g., pressure). As shown, sensors120 c and 120 d each have a horizontal component 120 c 1, 120 d 1, and avertical component 120 c 2, 120 d 2. As best shown in FIGS. 8A-8B, theIHU 22 may be fitted within the overliner 21, which is configured to bepositioned within the helmet 20 and designed to rest on the player'shead. The overliner 21 includes a thin layer of padding 21 a that ispositioned between the sensor assembly 120 and the wearer's head whenthe helmet is worn by the wearer. Additionally, as shown in FIG. 8B, anenergy attenuation assembly 20 c resides outside of and adjacent to thesensor assembly 120. Further, as shown in FIG. 10, the shell 20 a is theoutward most layer and the sensor assembly 120 is positioned within theshell 20 a without contacting the shell 20 a. Thus, when the IHU 22 isinstalled within the helmet 20 and the helmet 20 is worn by the wearer,the shell 20 a is the outward most layer, then the energy attenuationassembly 20 c is the next outermost layer, then the sensor assembly 120is next outermost layer, then the overliner 21 is the next outermostlayer, and finally the wearer's head is the next outermost layer or theinnermost layer. It should be understood, that in this configuration thesensor assembly 120 is removable from the helmet 20 and does notdirectly touch the player's head.

Referring back to FIG. 8A, the overliner 21 also includes coupling means21 b to couple the sensor assembly 120 within the overliner 21. Thiscoupling means 21 b may include loops or straps 21 c that can extendfrom one side of a portion of the overliner 21 to the other side of theoverliner 21. Additionally, the coupling means 21 c may include pockets21 d that are designed to receive an extent of the sensor assembly 120.It should be understood that the coupling means 21 c may take otherforms, such as snaps, Velcro, other mechanical connectors, orchemical-based connectors that allow the sensor assembly 120 to beremoved from the overliner 21.

In a slightly different implementation, the sensor assembly 120 ispositioned adjacent to the innermost portion of the overliner 21, suchthat when the overliner 21 is positioned within the player's helmet 20,there substantially no padding material that is positioned between theplayer's head and the sensor assembly 120. In this implementation, thesensor assembly 120 is positioned extremely close to the player's head,but is still not directly touching the player's head because the extentof the overliner 21 is still placed between the player's head and thesensor assembly 120. In yet another implementation, the sensor assembly120 may be placed in direct contact with the player's head when thehelmet is worn by the player.

FIG. 9A shows a partial top view of the helmet 20 that is configured toreceive the IHU 22, while FIGS. 9B-9C show cross-sectional views of thehelmet 20 shown in FIG. 9A. Specifically, the FIGS. 9B-9C show an energyattenuation assembly 20 c that omits an extent of the overliner 21 andan extent of the energy attenuation assembly 20 c is configured toreceive a portion of the sensor assembly 120. Specifically, FIG. 9Bshows a cross-sectional view of the stock helmet described within U.S.patent application Ser. No. 16/691,436, wherein the members containedwithin the energy attenuation assembly 20 c include different latticeregions that are comprised of different lattice cells. Additionaldetails about these members are described within U.S. patent applicationSer. No. 16/691,436, which is incorporated herein by reference. FIG. 9Cdoes not show lattice structures to make viewing of the slots within theenergy attenuation members easier to see; however, it should beunderstood that these members within the energy attenuation assembly 20c include lattice structures. To receive the portion of the sensorassembly 120, a plurality of slots 17 are formed within the energyattenuation assembly 20 c. In particular, a front member 23 of theenergy attenuation assembly 20 c includes a slot 19 a and jaw members 25of the energy attenuation assembly 20 c includes a slot 19 b. As shownin FIGS. 9B-9C, the plurality of slots 17 are formed a distance from theinner surface of the energy attenuation assembly 20 c, which ensuresthat there is some energy attenuation material that is positionedbetween the player's head and the sensor assembly 120, when the helmet20 is worn by the player.

Alternatively, the sensor assembly 120 may be integrally formed as partof the energy attenuation assembly 20 c. For example, the skin of eachmember of the energy attenuation assembly 20 c or the lattice structurecontained within each member of the energy attenuation assembly 20 c maybe coated with or integrally formed with a material that can act as asensor. Specifically, the skin or the lattice structure contained withinthe energy attenuation assembly 20 c may be composed of a material thatincludes carbon nanotubes blended within a light sensitive polyurethane.Additional information about this material and other possible materialsare discussed within N. Hu, et al. Investigation on sensitivity of apolymer/carbon nanotube composite strain sensor. Carbon, 48 (3) (2010),pp. 680-687 and Radeti, M. & Cortes, Pedro & Kubas, George & Cook, Jim &Gade, Ravi & Oder, T. (2016). The Sensing Properties of Fuzzy CarbonNanotube Based Silica Fibers: Ceramic Transactions, Volume CCLX.10.1002/9781119323624.ch13, both of which are fully incorporated hereinby reference. It should be understood that a combination of the abovedescribed sensor placements may be utilized within a helmet, wherein onesensor within the sensor assembly 120 may be placed within an energyattenuation member and another sensor within the sensor assembly 120 maybe integrally formed within the energy attenuation assembly 20 c. Itshould be further understood that the sensor assembly 120 may be placedin other locations within the helmet 20 and may be coupled to otherstructures within the helmet 20.

Although the IHU 22 is shown and described to include five sensors 120a-e within the sensor assembly 120, one of ordinary skill in the artrecognizes that the IHU 22 may have more or fewer sensors (e.g., between1 sensor and 100 sensors). The number of sensors may depend on theapplication and the information that is required to meet the needs ofthe application. For monitoring at least one physiological parameter ofplayer engaged in a sports activity, (e.g., American football), thelocation of pressure applied by an impact is useful in determining theseverity of the impact. In another application, the location may not beas important and in these applications a single sensor may be used. Forexample, a single sensor may be sufficient, if the IHU 22 is utilized todetermine when a single impact helmet should not be worn afterexperiencing an impact with a large enough magnitude.

i. Sensors Contained within the Sensor Assembly

In one exemplary embodiment, the sensors 120 a-120 e of the sensorassembly 120 are formed from an electret film, which has a unique,strong electromechanical response to an impact(s) to the helmet 20. Thefilm is based on a polyolefin material manufactured in a continuousbiaxial orientation process that stretches the film in two perpendiculardirections (machine direction and the transverse direction). Further thefilm is expanded in thickness at high-pressure gas-diffusion-expansion(GDE) process. The structure of electret film consists of flat voidsseparated by thin polyolefin layers. Typically the electret film is70-80 μm thick. The voids are made by compounding small particles, whichfunction as rupture nuclei and form closed lens like cavities to thefilm during the biaxial orientation. The voids are enlarged at with theGDE process, which more than doubles the thickness and elasticity of thefilm by increasing the size of air-voids inside it. Electromechanicalresponse with GDE processed film is over 10-fold compared to non-swelledfilm. A permanent electric charge is injected into the material bycorona charging it in a high electric field. This causes electricbreakdowns to occur inside the material, thus charging the voidinterfaces inside the film in order to form an electret material capableof interacting with its environment. Thin metal electrodes are, forexample, arranged by screen-printing them first to 75-100 μm polyesterfilm and laminating together with electret film. Vacuum evaporation toboth surfaces of the film is also possible for actuator purposes. Othertypical ways to arrange electrodes are using aluminum-polyester laminateand etching the electrode pattern prior to laminating with electretfilm. In another implementation, the sensors 120 a-120 e are made ofpiezoelectric material (e.g., Polyvinylidene Flouride (PVDF) and LeadZiconate Titanate (PZT)). It should be understood that other materialsor configurations of materials may be utilized to form the sensorswithin the sensor assembly.

b. Control Module Contained within the Helmet

FIG. 11 illustrates a schematic of the IHU 22. As shown, the controlmodule 130 is connected to each sensor 120 a-120 e via separate leads126. The control module 130 may include a signal conditioner 130 a, afilter 130 b, a microcontroller 130 c (or microprocessor), a telemetryelement 130 d, an encoder 130 e, a power source 130 f and an activitysensing module 130 g. Each of these elements is contained within thecontrol module 130 function together to measure at least onephysiological parameter, compare this measured physiological parameterusing various algorithms, and transmit the data captured by themeasurement of the physiological parameter upon determining that thecaptured information is above a predefined threshold value. Additionaldetails about these hardware modules and their functionality arediscussed below.

i. Signal Conditioner and Filter

The signal conditioner 130 a and a filter 130 b are utilized by thecontrol module 130, as necessary, to condition the signals that arereceived from the sensors 120 a-120 e. For example, the signalconditioner 130 a and filter 130 b may be utilized to filter out low andhigh-frequency noise that is generated by the sensors 120 a-120 e. Suchnoise may be introduced into the system due to environmental conditions,miss-alignment of the sensors 120 a-120 e within the helmet 20, or othersimilar factors. It should be understood that in some embodiments, asignal conditioner 130 a and filter 130 b may not be utilized or onlyone of these components may be utilized.

ii. Microcontroller

The microcontroller 130 c may be a processor that is specificallydesigned for use within the IHU 22. The microcontroller 130 c includesmemory that store the algorithms described in FIG. 12, receive thesignals from the signal conditioner 130 a and a filter 130 b, andanalyze these signals using the algorithm described in FIG. 12.Alternatively, the algorithms described within FIG. 12 may be storedwithin a separate memory contained within the IHU 22, which themicrocontroller 130 c may call upon once signals are received from thesignal conditioner 130 a and a filter 130 b. It should be understoodthat the microcontroller 130 c must have sufficient capability's toperform the calculations within FIG. 12 in less than 5 minutes,preferably less than 1 minute, more preferably less than 30 seconds, andmost preferably less than 10 seconds. This processing power is necessaryto ensure that: (i) the complex monitoring of physiological parameter(s)of multiple players is correctly performed, (ii) physiological parameterdata is rapidly computed and reported, and (iii) near-instantaneousdecisions can be made regarding whether remove a particular player fromthe activity prior to the start of the next play or sequence ofactivity. Without being able to perform these steps, including thenecessary monitoring and calculations, in real time or near real time,the system 10 will not adequately function. For example, if thecalculations took several minutes, then sideline personnel (e.g. coachesand athletic trainers) in American football would not know if a playersustained a suspect impact and should be removed from the course of playfor observation, which would potentially expose the player to additionalimpacts. Additionally, the delay in these calculations may accumulateover the duration of the sporting activity, which would increase thesedelays and render the system 10 largely ineffective for sportingactivity that occurs over an appreciable duration of time (e.g., hoursin the context of football, hockey or lacrosse). It should also beunderstood that a processor that includes too much processing power(e.g., state of the art desktop processor) will also render the system10 inoperable because this processor's power consumption are too highand will drain the battery 130 f of the IHU 22 before it can last asufficient amount of time (e.g., the length of the activity). Thebattery 130 f of the IHU 22 cannot be enlarged without regard to theconfiguration and footprint of the IHU 22 that must fit within thehelmet 20. Accordingly, the processing power of the IHU 22-namely, themicrocontroller 130 c-must be balanced to ensure that it can perform thenecessary calculations while lasting a sufficient amount of time.

It should also be understood that at least an extent of the datacollected by the sensor assembly 120 of the system 10 must be analyzedby the microcontroller 130 c within the helmet 20 and this data cannotbe sent to a person for performing the necessary calculations withintheir head or a central computer that is remotely located from thehelmet 20. For example, a hypothetical system that is designed totransfer all of the data collected by the sensor assembly 120 to acentral computer for analysis would not be able to function as thesystem 10 described herein for at least the following reasons. First,the hypothetical system's in-helmet unit would not know when to wake upand record the data because the hypothetical system would not know ifthe impact was over the noise threshold. Second, the hypothetical systemwould take too long to transfer and process this data on a remotecomputer or attempt to have a person perform these calculations, whichwould cause the same problems identified above. Third, increasing theamount of data that is transferred out of the helmet 20 will increasethe power requirements of the hypothetical system's in-helmet unit,which in turn will require the use of a larger battery to maintain thesame length of operational time between charges. However, increasing thebattery size is not permissible due to the space and footprintconstraints that apply to the hypothetical system's in-helmet unit. TheIHU 22 of the multi-functional system 10 can last multiple weeks andeven over one year without needing to be recharged. This is because: (i)in normal operation (e.g., continuous monitoring for alertable impacts),the IHU 22 may consume about 12-20 uA, (ii) in a deep sleep state (e.g.,everything is off except timekeeping), the IHU 22 may consume about 8uA, and (ii) in an alert state (e.g., the IHU 22 is trying to send analert to the alert unit 26) the IHU 22 may consume about 1-5 mA.Increasing the size of the battery to maintain these operational timeswill undesirably: (i) add weight to the IHU 22, (ii) make the IHU 22more expensive to manufacture, and (iii) require additional analysis todetermine if it is even possible to place a larger battery within thehelmet 20 without requiring alterations to the helmet's design.

iii. Telemetry Module

The IHU 22 uses the telemetry module 130 d to wirelessly connect andtransmit physiological parameter data to the receiving unit 24 viacommunication links 40, 50, 52, 54, 56 (shown in FIGS. 1-6).Specifically, the communication link 40, 50, 52, 54, 56 may be based onany type of wireless communication technologies. These wirelesscommunication technologies may operate in unlicensed bands (e.g., 433.05MHz-434.79 MHz, 902 MHz-928 MHz, 2.4 GHz-2.5 GHz, 5.725 GHz-5.875 GHz)or in a licensed band. A few examples of wireless communicationtechnologies that may be used include: Bluetooth (e.g., Bluetoothversion 5), ZigBee, Wi-Fi (e.g., 802.11a, b, g, n), Wi-Fi Max (e.g.,802.16e), Digital Enhanced Cordless Telecommunications (DECT), cellularcommunication technologies (e.g., CDMA-1X, UMTS/HSDPA, GSM/GPRS,TDMA/EDGE, EV/DO, or LTE), near field communication (NFC), or a customdesigned wireless communication technology. In other embodiments (FIG.5, which has an outdoor transmission range from 50 to 200 meters), thetelemetry module 130 d may include both wired and wireless communicationtechnologies. A few examples of wired communication technologies thatmay be used, include but are not limited to, any USB basedcommunications link, Ethernet (e.g., 802.3), FireWire, or any other typepacket based wired communication technology. Specifically,communications link 42 shown in FIG. 5 enables the transmission ofphysiological parameter data from the IHU 22 to the receiving device 24over a wired connection. It should be understood that the telemetryelement 130 d may include a single transmitter/receiver or multipletransmitters/receivers depending on the configuration and application ofthe system 10.

An example of a custom designed wireless communication technology thatis specifically designed for this application is described below. Thiswireless communication technology is based on a time division multipleaccess (TDMA) approach. Approximately every 9.6 seconds, the receivingunit 24 broadcasts a ping 75 times every 30 ms. After each ping, thereceiving unit 24 listens for an IHU 22 that is scheduled to respond ata specific ping set and time slot. There are two-time slots per pingwhere an IHU 22 can respond. The ping plus time slot listen period is a“superframe.” During setup, the IHU 22 is paired with the receiving unit24 to align the wireless communication settings (e.g. channel, PAN ID,etc) as well as a timeslot within a superframe between these devices.Since communications are happening asynchronously and the actualcommunication time is in a small window, the IHU 22 wakes upperiodically at some multiple of ping windows (the 75 superframes). Ifit hears a ping from it's receiving unit 24, the IHU 22 calculates thetime required to wakeup on the next ping cycle (9.6 sec+an offset itcalculates to wakeup right before the appropriate superframe). After anIHU 22 checks in with the receiving unit 24, the receiving unit 24responds with an acknowledgment. Included in this acknowledgment are theplayer's 2^(nd), 3^(rd) and 4^(th) thresholds. The IHU 22 monitors theimpacts on the player wearing the IHU 22 and reports an alert to thereceiving unit 24, if the impacts exceed the 3^(rd) or 4^(th)thresholds. Is should be understood that other multiplexing techniquesmay be used instead of TDMA, such as code division multiple access(CDMA), or frequency division multiple access (FDMA) technology. Also,system 10 may analyze the operating environment and pick a channel thathas the least amount of noise that is within the operating range of theradios/transmitters. Once a new frequency has been identified, thereceiving unit 24 instructs all or a subset of the telemetry modules 130d contained within the IHUs 22 to switch over to this new frequency.

iv. Encoder and Power Source

The encoder 130 e is designed to encode the alert data and/or the impactdata before the telemetry modules 130 d transmit this data.Specifically, the encoding of this data includes adding a uniqueidentifier to the data, such as player number, name, the serial numberof the IHU 22, the location of the player on the team roster, and etc.While the encoder 130 e is shown as separate from the telemetry element130 d, the encoder 130 d can be integrated within the telemetry element130 d or the microcontroller 130 c. In certain embodiments, encoder 130e can encrypt the data to increase the security of the data. The powersource 130 e may be designed to last at least as long as the activity,preferably as long as a week, more preferably more than a week. To meetthis design requirement, the system 10 can utilize a non-removablerechargeable battery, removable rechargeable battery, or a removablenon-rechargeable battery.

v. Activity Sensing Module

The activity sensing module 130 g may be used to turn the IHU 22 ON orOFF based on the movement of the helmet 20 that it is installed therein.For example, moving the helmet over a predefined amount of time, hittingand/or shaking the IHU 22 will turn ON. If the helmet has not moved foranother predefined amount of time (e.g., 7 minutes) or has not beenhitting and/or shaking for a predefined amount of time the IHU 22 willturn OFF. It should be understood that the activity sensing module 130 gwill default to the one position even when the movement of the helmet isextremely low, as this helps ensure that the IHU 22 is activated duringthe activity. Alternatively, the helmet 20 may have control buttons,such as a power button and a configuration button. In a furtheralternative, the IHU 22 may turn ON and OFF based on a shaking pattern,proximity to a radio beacon (e.g., Wi-Fi beacon, Bluetooth, etc.), atimer, a completion of a circuit based on the connection of the player'schin strap, pressure on an extent of the sensor assembly 120, acombination of the above, or other similar methods of turning ON and OFFan electronic circuit in close proximity to a player's head.

c. IHU Performs the Algorithms Shown in FIG. 12

As discussed above, the IHU 22 and the algorithms shown in FIG. 12 inthis exemplary embodiment are designed to determine an impact magnitudevalue from the pressure applied to a player's head as a result of animpact the player experienced while engaged in a contact sport.

i. Threshold Values/Ranges Utilized by the IHU

To determine the impact magnitude value, multiple thresholdvalues/ranges are programmed into the memory of the IHU 22. Some ofthese threshold values/ranges are standardized across all IHUs 22. Inother words, the same or non-custom threshold value is programmed intoeach and every IHU 22. For example, a predetermined noise threshold anda 1^(st) threshold/an impact matrix threshold are standardized valuesthat are the same across all IHUs 22. These values can be standardizedand do not need to be tailored to a player who is going to utilize theIHU 22 because these values are only used to determine if an impactoccurred and if that impact has a magnitude that is high enough towarrant analysis.

Other threshold values/ranges are not standardized across all IHUs 22.In other words, different or custom threshold values/ranges may beprogrammed into the IHU 22. The non-standardized or custom thresholdvalues/ranges are based upon information that is entered or obtainedfrom the player who is going to utilize the IHU 22. In particular,tailoring the IHU 22 to the specific player is desirable because thepressure that is applied to a player's head during the course of theactivity varies between playing levels, such as the pressure levels seenat the youth level in comparison to the levels seen at the NFLprofessional level. Tailoring the IHU 22 to the specific player byinputting non-standardized or custom threshold values/ranges within theIHU 22 creates a specialized machine with specialized functionality todetermine the impact magnitude value for impacts that the specificplayer(s) experiences during the activity. This specialized IHU 22provides more accurate information, thereby providing a significanttechnical improvement to the system 10. Additionally, the accuracy ofthe information provided by the specialized IHU 22 improves themonitoring capabilities of the system 10 and the efficiency of theauthorized user's ability to make suggested changes in how the monitoredplayer(s) engages in the physical activity.

The non-standardized or custom threshold values/ranges in thisembodiment are: (i) 2^(nd) threshold or high magnitude impact threshold,(ii) 3^(rd) threshold or single impact alert, (iii) 4^(th) threshold ora cumulative impact alert threshold and (iv) ranges of impact magnitudevalues that are associated with the severity values. The system 10determines the values of these non-standardized threshold values/rangesbased on the player's position (e.g., offensive line, running backs,quarterback, wide receivers, defensive linemen, linebackers, defensivebacks and special teams) and level (e.g., youth, high school, collegeand professional players). For example, the system 10 may have between 5and 100 different values for each threshold/value, preferable between 20and 60 different values for each threshold/value, and most preferablybetween 25 and 40 different values for each threshold/value. To selectthe proper custom thresholds/values, a player or authorized user willset up the IHU 22 by programming the player's level and position intothe remote terminal 28. The remote terminal 28 pulls the customthresholds/values from the team/national database 32, 38 that correspondto the player's level and position. In particular, these correspondingvalues were previously determined based on a statistical analysis ofdata that have been collected using the proprietary technologies ownedby the assignee of the present Application and are disclosed in U.S.Pat. Nos. 10,105,076, 9,622,661, 8,797,165, and 8,548,768. After thecustom thresholds/values have been determined, these thresholds/valuesare sent to the receiving device 24 in order to relay this informationto the IHU 22. Overall, it should be understood that a player who playsoffensive line at the youth level will have non-standarized or customthreshold values/ranges that are different than a player who playsrunning back at the NCAA level.

In an alternative embodiment, system 10 may select a set of customthresholds/values based on the player's level and position that isentered into the system 10 using the remote terminal 28. After theplayer has experienced over a predefined number of impacts (e.g., over50 impacts and preferably over 100 impacts), the system 10 may adjustthe custom thresholds/values or may select a different set of customthresholds/values from the team/national database 32, 38. Thisadjustment or the selection of a different set of customthresholds/values will aid the system 10 in providing more accurateinformation to the authorized user. For example, a player who plays twodifferent positions (e.g., quarterback and linebacker) can only selectone of these positions during the initial setup, which in turn may leadto over/under-reporting of alerts because the selection of customthresholds/values were not specifically tailored to the individualplayer's playing style. In another example, a player who playsquarterback may not experience impacts that a quarterback wouldtypically experience based on the player's playing style. For example,the quarterback may run the ball an unusual amount of times, whichcauses the quarterback to experience impacts that are more similar to arunning back than a quarterback. In a further example, a player who hasa medical condition may be susceptible to high magnitude impacts;therefore, the custom thresholds/values should be adjusted to accountfor these medical conditions. Thus, this adjustment or the selection ofa different set of thresholds/values can use the information from theexperienced impacts to help ensure that the proper customthresholds/values are selected for the player.

Adjustments to the selected custom thresholds/values may be made using alearning algorithm that takes into account at least a plurality of thefollowing: (i) the player's position and level, (ii) the impacts theplayer has experienced, (iii) the preset custom thresholds/valuescontained within the team/national database 32, 38, (iv) player'smedical condition(s), and/or (v) videotape analysis. Alternatively, theadjustments to the selected custom thresholds/values may be made byreducing or increasing the selected thresholds/values be a certainpercentage that is based on the percentage over the mean impact levelsthat are experienced by players who play at a similar level andposition. For example, if the player is experiencing impact levels thatare 10% over the mean impact levels for other similar players, then theplayer's thresholds/values are increased by 10%. It should be understoodthat these adjustments to the thresholds/values or selection of adifferent set of thresholds/values (e.g., running back values instead ofquarterback values) may be provided to the authorized user to ensurethat the authorized user desired to make this change or may beautomatically changed without the authorized user's input or knowledge.Also, the authorized user may request that the system 10 perform theabove analysis on any player, even if the system does not detect orsuggest such a change. This may be beneficial if an authorized userbelieves that the player is not experiencing enough alertable impacts.

ii. IHU Detects an Impact

The IHU 22 includes five distinct sensors 1220 a-e for five distinctregions (e.g., top, left, right, front, and back) of the player's head.Specifically, FIG. 20 shows these regions on a graphical representationof a player's head. The IHU 22 continually monitors for a value from anysensor 120 a-e that exceeds the predetermined noise threshold, which isprogrammed into the IHU 22. Once the IHU 22 determines that a sensor 120a-e has recorded a value that is greater than the predetermined noisethreshold, the IHU 22 detects there is an impact. This detection causesthe microcontroller 130 c to wake up and record data from all sensors120 a-e. When an impact to the helmet 20 is detected by multiplesensors, only data from the closest sensor to the impact location andthe sensors adjacent to the closest sensor are used in calculations thatwill be discussed below. For example, when an off-center impact isexperienced on the helmet 20 and the back sensor 120 e and left sensor120 c detect equal impact energy without significant energy from othersensors, then the impact location is determined by the system 10 to bedirectly between the back sensor 120 e and left sensor 120 c. Also, ifthe impact location is on the left side of the helmet 20, the system 10will combine usable data from the left 120 c, front 120 a, top 120 b,and rear 120 c sensors to calculate the impact magnitude value from therecorded physiological parameter values. However, any data recorded bythe right 120 d sensor will be ignored and not included in this impactmagnitude value. Similarly, when an on-center impact is applied to thefront of the helmet 20, any data from the rear sensor 120 e is ignoredand not included in the impact magnitude value. Accordingly, the system10 is configured to selectively utilize data from a limited number ofsensors 120 a-e to create an impact magnitude value, while disregardingother, essentially irrelevant sensor data, based upon the location ofthe impact to the helmet 20. Ignoring this irrelevant data may besignificant because it helps ensure that the impact magnitude value isproperly calculated. Without doing such, the impact magnitude value maybecome skewed by the player's head resonating within the helmet 20.

iii. IHU Performs Head Impact Exposure Algorithm

After the microcontroller 130 c determines the impact magnitude value,the microcontroller 130 c will simultaneously perform both algorithmsshown in FIG. 12. The first algorithm or head impact exposure (HIE)algorithm 500 does not weight the impact magnitude value based on thelocation of the impact, while the second algorithm or alert algorithm502 may weigh the impact magnitude value based on the location of theimpact and other factors. The first algorithm or HIE algorithm 500compares the impact magnitude value to the 1^(st) threshold or an impactmatrix threshold in step 530. The 1^(st) threshold or an impact matrixthreshold is set between 1 g and 80 gs and preferably between 5 gs and30 gs. If the impact magnitude value is less than the impact matrixthreshold, than the microcontroller 130 c will disregard the impactmagnitude value. However, if the impact magnitude value is greater thanthe impact matrix threshold, than the microcontroller 130 c will add theimpact magnitude value to the impact matrix in step 532. Examples ofimpact matrixes are shown in FIG. 21. Specifically, the impact matrixmay be comprised of 5 columns and 5 rows, where the 5 columns correspondto the location of the impact on the player's head (e.g., front, back,left, right, and top) and the 5 rows correspond to the severity of theimpact (e.g., 1^(st), 2^(nd), 3^(rd), 4^(th) or5^(th) severity). Asdiscussed above, FIG. 20 shows a graphical representation of a player'shead and where each of these regions is located.

Each of these severity values (e.g., 1^(st), 2^(nd), 3^(rd), 4^(th) or5^(th)) corresponds to a range of impact magnitude values that areprogrammed into the IHU 22. The 1^(st) range may include impactmagnitude values between the impact matrix threshold and the 50^(th)percentile of historical impact magnitude values for players of similarposition and level, the 2^(nd) range may include impact magnitude valuesbetween the 51^(st) percentile and the 65^(th) percentile of historicalimpact magnitude values for players of similar position and playinglevel, the 3^(rd) range may include impact magnitude values between the66^(th) percentile and the 85^(th) percentile of historical impactmagnitude values for players of similar position and playing level, the4^(th) range may include impact magnitude values between the86^(th)percentile and the 95^(th) percentile of historical impact magnitudevalues for players of similar position and playing level, and the 5^(th)range may include impact magnitude values above the 95^(th) percentileof historical impact magnitude values for players of similar positionand playing level.

Once the microcontroller 130 c has added the impact magnitude value tothe impact matrix in step 532, as shown in FIG. 19, the microcontroller130 c determines if a 1^(st) predefined amount of time or an impactmatrix transmit time period has passed from the time the IHU 22 lasttransmitted the impact matrix to a receiving device 24. The impactmatrix transmit time period may be set to any time, preferably it is setbetween one second and 90 days and most preferably between 30 secondsand 1 hour. If the amount of time that has passed since the unit lasttransmitted the impact matrix to a receiving device 24 is less than orgreater than the impact matrix transmit time period, then themicrocontroller 130 c will include this most recent impact within theimpact matrix and then will perform no additional steps. However, if theamount of time that has passed since the unit last transmitted theimpact matrix to a receiving device 24 is equal to the impact matrixtransmit time period, then the control module 130 of the IHU 22 willtransmit the impact matrix from the IHU 22 to a receiving device 24(e.g., an alert unit 26, a remote terminal 28, a controller 30, anetwork element 34, or a combination of these items and other items) instep 536. Upon the completion of this decision, the IHU 22 has finishedperforming the HIE algorithm 500. It should be understood that theimpact matrix may be transmitted at any time and does not require thedetection of an impact.

iv. IHU Performs the Algorithms Shown in FIG. 12

While the IHU 22 is performing the HIE algorithm 500, the IHU 22 is alsoperforming the alert algorithm 502. First, the microcontroller 130 cwill calculate an impact value by weighting the impact magnitude valuebased on the location of the impact and other factors in step 538. Inone exemplary embodiment, the impact value is calculated by firstdetermining the linear acceleration, rotational acceleration, headinjury criterion (HIC), and the Gadd severity index (GSI) for the givenimpact. The algorithms used to calculate these values are described inCrisco J J, et. al. An Algorithm for Estimating Acceleration Magnitudeand Impact Location Using Multiple Nonorthogonal Single-AxisAccelerometers. J BioMech Eng. 2004; 126(1), Duma S M, et. al. Analysisof Real-time Head Accelerations in Collegiate Football Players. Clin JSport Med. 2005; 15(1):3-8, Brolinson, P. G., et al. Analysis of LinearHead Accelerations from Collegiate Football Impacts. Current SportsMedicine Reports, vol. 5, no. 1, 2006, pp. 23-28, and Greenwald R M,et., al. Head impact severity measures for evaluating mild traumaticbrain injury risk exposure. Neurosurgery. 2008; 62(4):789-798, thedisclosure of which is hereby incorporated by reference in its entiretyfor all purposes. Once the linear acceleration, rotational acceleration,head injury criterion (HIC), and the Gadd severity index (GSI) arecalculated for a given impact, these scores are weighted according tothe algorithm set forth in Greenwald R M, et., al. Head impact severitymeasures for evaluating mild traumatic brain injury risk exposure.Neurosurgery. 2008; 62(4):789-798, the disclosure of which is herebyincorporated by reference in its entirety for all purposes. Thisresulting weighted value is the impact value. In this exemplaryembodiment, the impact value is also equal to what has been called aHITsp value. While not diagnostic of injury, HITsp values are moresensitive and specific to diagnose concussions than any of the componentmeasures alone.

In other exemplary embodiment, the impact value may be equal to only thelinear acceleration for the given impact. In a further exemplaryembodiment, the impact value may be equal to only the HIC score for thegiven impact. In another exemplary embodiment, the impact value may beequal to only the rotational acceleration for a given impact. In anotherexemplary embodiment, the impact value may be equal to the linearacceleration weighted by a combination of impact location and impactduration. In another exemplary embodiment the impact value may be equalto the weighted combination of linear acceleration, rotationalacceleration, HIC, GSI, impact location, impact duration, impactdirection. In another exemplary embodiment, the impact value may beequal to a value that is determined by a learning algorithm that istaught using historical data and diagnosed injuries. In even a furtherexemplary embodiment, the impact value may be equal to any combinationof the above.

Once the impact value is calculated in step 538 by the microcontroller130 c, the impact value is compared against a 2^(nd) threshold or highmagnitude impact threshold in step 540. This high magnitude impactthreshold may be set to the 95^(th) percentile for impacts recorded byplayers of similar playing level and similar position. If the impactvalue is less than the high magnitude impact threshold, than themicrocontroller 130 c will not perform any additional steps. However, ifthe impact value is greater than the high magnitude impact threshold,than the impact value will be added to the cumulative impact value instep 542 and compared against a 3^(rd) threshold or single impact alertthreshold in step 544. This single impact alert threshold may be set tothe 99^(th) percentile for impacts recorded by players of similarplaying level and position.

If the impact value is greater than the single impact alert threshold,than the control module 130 of the IHU 22 transmits alert data that isassociated with the single impact alert to the receiving device 24(e.g., an alert unit 26, a controller 30, a network element 34, or acombination of these items and other items) in step 546. The alert datamay include, but is not limited to,: (i) the impact value, (ii) impactlocation, (iii) impact time, (iv) impact direction, (v) player's uniqueidentifier, (vi) alert type, (vii) player's heart rate, (viii) player'stemperature, (ix) impact magnitude and/or (ix) other relevantinformation (e.g., activity information). As will be discussed ingreater detail below, the alert data may be displayed on the alert unit26 in a graphical (e.g., on a representative image of a player) manneror in a non-graphical (e.g., numerical value) manner. If the impactvalue is less than the single impact alert threshold, than themicrocontroller 130 c will not perform any additional steps along thispath of the algorithm 502.

While the microcontroller 130 c is determining whether the impact valueis greater than the single impact alert threshold in step 544, themicrocontroller 130 c further calculates a weighted cumulative impactvalue that includes this new impact value, in step 548. Specifically,the weighted cumulative impact value is calculated based on a weightedaverage of every relevant impact value that is over a 2^(nd) thresholdor high magnitude impact threshold. To determine this weighted average,every impact value that is over a 2^(nd) threshold is weighted by adecaying factor. For example, an impact that was recorded 4 days ago maybe multiplied by 0.4 decaying factor, thereby reducing the magnitudelevel of this impact. After the weighted impact values are determined,these values are summed together to generate the weighted cumulativeimpact value. It should be understood that the microcontroller 130 cwill exclude irrelevant impact values that are old enough to cause theirweighted impact value to be zero due to the decaying factor. Forexample, if the decaying factor for an impact that is over 7 days old is0; then regardless of the impact value, this impact is irrelevant tothis calculation and will not be included within this calculation. Oneskilled in the art recognizes that weighting variables (e.g., timewindow, decay function, input threshold) can be adjusted to values otherthan then values disclosed herein. For example, the decaying factor foran impact that is over 14 days old is 0, while the decaying factor foran impact that is over 7 days old is in 0.5. In a further example, thedecaying factor for an impact over a set time period may be non-linear.

Once the weighted cumulative impact value has been calculated in step548, this value is compared against a 4^(th) threshold or a cumulativeimpact alert threshold in step 550. This cumulative impact alertthreshold may be set to the 95^(th) percentile for weighted cumulativeimpact values recorded by players of similar playing level and position.If the weighted cumulative impact value is less than the cumulativeimpact alert threshold, than the microcontroller 130 c will not performany additional steps. However, if the weighted cumulative impact valueis greater than the cumulative impact value threshold, than the controlmodule 130 of the IHU 22 transmits alert data that is associated with acumulative impact alert to the receiving device 24 (e.g., an alert unit26, a controller 30, a network element 34, or a combination of theseitems and other items) in step 552. Upon the completion of thisdecision, the IHU 22 has finished performing the alert algorithm 502.

The system 10 is also configured to monitor impacts and process datafrom players who experience multiple impacts on the same play. A personof skill in the art of designing sophisticated monitoring equipment forcontact sports recognizes that many football players, including runningbacks, offensive lineman and defensive lineman, experience multipleimpacts on a single play. For example, when a running back experiencesmultiple impacts while carrying the football (e.g., a rushing play), thealgorithm in FIG. 12 is completed every impact detected by IHU 22, aslong as a predetermined time period or an impact time period (e.g., 60ms) has passed between impacts. In the context of the running backexperiencing two impacts, if both detected impacts exceed a 3^(rd)threshold or a single impact alert threshold, each impact is treated asan independent peak overexposure alert and the alert data is transmittedto the alert unit 26. It should be understood that based on the impactsexperienced by the player, the IHU 22 and specifically the controlmodule 130 may send a cumulative impact alert to the experiencing device24 without sending a single impact alert. Likewise, the control module130 may send a single impact alert to the receiving device 24 withoutsending a cumulative impact alert. Further, the control module 130 maysend an impact matrix to the receiving device 24 without sending eithera single or cumulative impact alert.

Unlike conventional systems (e.g., the systems disclosed within U.S.Pat. Nos. 10,105,076, 9,622,661, 8,797,165, and 8,548,768), themulti-functional system 10 disclosed herein performs multiple algorithmswithin the IHU 22 contained within the player's helmet 20 in order tomonitor that player and provide the authorized user with informationabout the monitored player's recently recorded physiological parameterdata, including how that data may differ from the monitored player'spreviously recorded physiological parameter data or how thephysiological parameter data that has been recorded in connection withother players. This functionality is beneficial because it providesinformation to the authorized user about what the player is actuallyexperiencing on-field, in comparison to his past playing experiences andhistory and other similar players, which in turn allows the authorizeduser-coaching staff or athletic trainers to identify and correct theplayer's playing techniques or provide aid to the player. Additionally,the multi-function system 10 provides information that the authorizeduser can share with the player to provide support for what theauthorized user is telling the player. Overall, this multi-functionsystem 10 provides many significant advantages over other conventionalsystems, which enable the system 10 to improve how the authorized usertrains, practices, and handles players, practice sessions and games.

3. Alternative Embodiments of the IHU

Instead of or in addition to monitoring and recording physiologicalparameter data related to pressure on a player's head as a result of animpact, the IHU 22 may monitor and record data related to otherphysiological parameters. For example, another physiological parameterthat may be analyzed by the system 10 is the player's temperature. TheIHU 22 may measure the player's temperature by including at least onetemperature sensor within the sensor assembly 120 of the IHU 22.Specifically, the temperature sensor may be a thermistor, whichcomprises resistive circuit components having a high negativetemperature coefficient of resistance so that the resistance decreasesas the temperature increases. Alternatively, the temperature sensor maybe a thermal ribbon sensor or a band-gap type integrated circuit sensor.Using one of these temperature sensors, the player's temperature may berecorded when an impact is detected and that impact resulted in either:(i) the impact magnitude being included in the impact matrix, (ii) atransmission of alert data based on a single alert, or (iii) atransmission of alert data based on a cumulative alert. In this example,the number of columns in the impact matrix may be doubled to include atemperature column that is located adjacent to each of the impactlocations (e.g., front, back, left, right, and top). This would allowfor the player's temperature to be recorded within these cells. Ifmultiple impacts were experienced that have the same severity level andlocation, the temperature measurements for those specific impacts may beaveraged together to create an average temperature for that specificseverity level and location.

In an alternative example, a player's temperature may be activelymonitored to determine if the player's temperature exceeds or dropsbelow a threshold. Specifically, an alert may be transmitted to thereceiving device 24 to inform sideline personal that a player isoverheating and the player should be pulled out of the activity to allowthe player to cool down. In a further example, the player's temperaturemay be periodically recorded and transmitted to the receiving device 24.These recordings can then be later analyzed by sideline personal tosuggest different equipment configurations or different workoutregiments in order to optimize the player's thermal management.

Instead of or in addition to monitoring and recording physiologicalparameter data related to: (i) pressure on a player's head as a resultof an impact or (ii) the player's temperature, the IHU 22 may alsomonitor and record data related to other physiological parameters. Forexample, another physiological parameter may be the player's heart rateand/or blood pressure. The IHU 22 may measure the player's heart rateand blood pressure by including at least one microelectromechanicalsystem (MEMS) type sensors that use auscultatory (e.g., listening to theinternal sounds made by the body) and/or oscillometric (e.g.,oscillations of the arterial pulse) within the sensor assembly 120 ofthe IHU 22. Using one of these heart rate and/or blood pressure sensors,the player's heart rate and/or blood pressure may be recorded when animpact is detected and that impact resulted in either: (i) the impactmagnitude being included in the impact matrix, (ii) a transmission ofalert data based on a single alert, or (iii) a transmission of alertdata based on a cumulative alert. In this example, the number of columnsin the impact matrix may be increased by 3 to include a heart ratecolumn and a blood pressure column that is located adjacent to each ofthe impact locations (e.g., front, back, left, right, and top). Thiswould allow for the player's heart rate and blood pressure to berecorded within these cells. Once multiple impacts were experienced thathave the same severity level and location, the heart rate and bloodpressure measurements for those specific impacts may be averagedtogether to create an average heart rate or blood pressure for thatspecific severity level and location.

In an alternative example, a player's heart rate and/or blood pressuremay be actively monitored to determine if the player's heart rate and/orblood pressure exceeds or drops below a threshold. Specifically, analert may be transmitted to the receiving device 24 to inform a traineror a coach than a player's heart rate is too high and the player shouldbe pulled out of the activity to allow the player to lower their heartrate. In an alternative example, the player's heart rate and/or bloodpressure may be periodically recorded and transmitted to the receivingdevice 24. These recordings can then be later analyzed by a trainer or acoach to suggest different equipment configurations or different workoutregiments in order to optimize the player's management of their heartrate and/or blood pressure.

Instead of or in addition to monitoring and recording physiologicalparameter data related to: (i) pressure on a player's head as a resultof an impact, (ii) the player's temperature, (iii) the player's heartrate or (iv) the player's blood pressure, the IHU 22 may also monitorand record data related to the player's balance. The IHU 22 may measuresmall movements by the player by using at least one low acceleration(low G) accelerometer within the sensor assembly 120 of the IHU 22.Using this low G accelerometer, small movements by the player may bemeasured to detect balance issues. Specifically, the IHU 22 may includean algorithm that calculates and observes a player's balance betweenplays or during extended stoppages in play, such as when a penalty isbeing assessed or a timeout. When a player assumes the ready positionprior to the commencement of the play, for example a three-point stance,the low G accelerometers and the algorithm would detect player movementsindicative of balance issues. If the IHU 22 detects that the player hasa balance issue, then an alert can be sent from the IHU 22 to thereceiving device 24 to alert the authorized user of this balance issue.

Instead of or in addition to monitoring and recording physiologicalparameter data related to: (i) pressure on a player's head as a resultof an impact, (ii) the player's temperature, (iii) the player's heartrate or (iv) the player's blood pressure, (v) player's balance, the IHU22 may also monitor and record data related to the player's O2saturation (Sp02%) or molecular quantiles of certain substancescontained within the player's blood (e.g. lactic acid, blood sugar). TheIHU 22 may measure O2 saturation or quantiles of certain substancescontained within the player's blood using optical sensing such as PPG(photoplethysmogram). Specifically, the IHU 22 may include an algorithmthat calculates and observes a player's O2 saturation or quantiles ofcertain substances contained within the player's blood. If anyone ofthese values is measured to be outside of the 95% for similar players,then an alert can be sent from the IHU 22 to the receiving device 24 toalert the authorized user of the issue. Alternatively, these valuescould be recorded upon the detection of an alerting event and may betracked to gain additional insight into these levels during an alertingevent.

Instead of or in addition to monitoring and recording physiologicalparameter data related to: (i) pressure on a player's head as a resultof an impact, (ii) the player's temperature, (iii) the player's heartrate, (iv) the player's blood pressure or (v) player's balance, (vi)player's O2 saturation (Sp02%) or molecular quantiles of certainsubstances contained within the player's blood (e.g. lactic acid, bloodsugar), the IHU 22 may also monitor and record data related to otherphysiological parameters. For example, the activity sensing module 130 gmay include multiple hardware components (e.g., (i) accelerometer, (ii)gyroscope, (iii) GPS sensor/indoor location sensors, (iv) amagnetometer, and/or (v) a heart rate monitor, such as the one discussedabove) to determine the activity levels and events that occurred duringthe game or activity. Specifically, information for these sensors can beprocessed to determine a player's: (i) acceleration of the player, (ii)deceleration of the player, (iii) velocity of the player, (iv) directionthe player is running (e.g., forward, laterally, backward, etc.), (v)when a player left the ground, (vi) sharp changes in direction whilerunning, and/or (vi) other general strength and condition metrics.

Using the above-identified information that was derived from datacollected by the activity sensing module 130 g, the system 10 coulddetermine player and/or team metrics, which include: (i) when a practicestarted, (ii) what drills likely occurred during the practice, (iii) howhard the player is working during practice (e.g., duration of time theplayer is standing, duration of time the player is jogging, and durationof time the player is running), (iv) how accurate where the player'sroutes he ran, (v) number of times the QB to a 3 step drop, 5 step dropor scrambled from the pocket and (vi) other desirable metrics. Examplesof how the system 10 could use the information from the sensorscontained within the activity sensing module 130 g to determine whenpractice started and ended could be done by setting a threshold numberof player's (e.g.,25% of the team) that have registered a minimal amountof movement. Additionally, the system 10 could determine the drills thatlikely occurred during the practice by recording at least a plurality ofthe following parameters: heart rate, movement of the players, impactdata, and/or alert data. Then a learning algorithm may be utilized tocompare these recently recorded values with values that have beenpreviously associated with certain drills. Based on this comparison, thesystem 10 can make a prediction of what the drills that the player/teamis likely doing at a given time.

Further, determining how hard a player is working during a workout couldbe based on his heart rate level, acceleration/deceleration levels,velocity levels, distance traveled, or a combination of one or more ofthese levels. Moreover, determining the accuracy of the routes a playerran could be done by analyzing the data that is gathered by theaccelerometer, gyroscope, GPS sensor/indoor location sensors, amagnetometer and comparing that against a set of identified plays. Forexample, the authorized user may input a drill into the practice plannerthat sets out 10 plays that will be run during that drill period. Thedata gathered by the above sensors can then determine the location ofthe player during the drill period and it can compare the player'slocation against a predicted location. The difference between thepredicted and actual locations will provide accuracy measurement. Fromthese examples it should be clear to one of skill in the art that otherinformation may be derived from the data that is generated by theactivity sensing module 130 g to provide additional information to theauthorized user.

The player/team metrics can be used by the system 10 to help provide amore complete picture of what the player is experiencing on the field,which can be used by the coaching staff to improve the training of theplayer/team. For example, system 10 may inform the authorized user thata certain set of drills carry a high impact load and only a few playershave a high activity level during this drill, which may suggest to thecoach that a different drill should be utilized. Additionally, after thesystem 10 informs the authorized user that a wide receiver has a poorroute quality, the coaching staff can use this information to suggestdrills that will improve the player's routes. Overall, providingadditional actionable data in a usable format to the authorized userwill improve the coach's ability to improve his player's playing leveland help ensure that his players are in the best position to besuccessful.

It should be understood that other contextual data or data that is notrelated to the player's physiological parameter data may be recorded bythe IHU 22 or an external device. For example, a temperature sensingdevice could be utilized to determine the relative heat index during themonitoring session relating to practice or game play. This data maysuggest that an authorized user move training to an alternate locationor time that has less risk exposure to harmful conditions, such as heatstroke. Other contextual data that may be monitored and recorded by thesystem 10 includes general fatigue, travel, sleep, blood/spit work,prescribed drugs (e.g., insulin, blood thinners). This data may beutilized to make suggested changes to coaching plans, such as practiceplans.

4. Receiving Device that is External from the Helmet

In the embodiment shown in FIG. 1, the receiving device 24 is an alertunit 26 and the physiological parameter data (e.g., (i) pressure, suchas alert data and impact matrixes (ii) temperature, (iii) heart rate,(iv) blood pressure, (v) balance, or other similar physiologicalparameters, such as data recorded by the activity sensing module 130 gor information derived therefrom) is transmitted from the IHU 22 to thereceiving device 24 via communications link 40. Specifically, thealerting unit 26 is hand-held, portable and is typically carried by aperson that is: (i) positioned proximate (e.g., within 50 yards) to thefield or location that the physical activity is taking place and (ii) isnot engaged in the physical activity. Additional information about thealerting unit 26 will be discussed below. The receiving device 24includes various components typically contained within the belowdescribed exemplary embodiments of the receiving device 24, such as adisplay, a processor, a memory, peripheral devices, aradio/transmitter/receiver, sensors, and other components. It should beunderstood that the radio/transmitter/receiver of receiving device 24,must be the same as or compatible with the radio/transmitter/receiver130 d contained within the IHU 22. In other words, theradio/transmitter/receiver contained within the receiving device 24 mayutilize any type of wired or wireless communication technologies thatare discussed above in connection with the IHU 22 which the IHU 22utilizes.

In the embodiment shown in FIG. 3, the receiving device 24 is acombination of: (i) an alerting unit 26, (ii) a remote terminal 28, and(iii) a team database 32. In this embodiment, physiological parameterdata is transmitted from the IHU 22 to this receiving device 24 viacommunications link 42. As such, the receiving device 24 in thisembodiment may be hand-held and portable in some instances and istypically positioned around (e.g., within 150 yards) the field orlocation that the physical activity is taking place. While the receivingdevice 24 may be positioned around the field or location, the receivingdevice 24 in this embodiment is not typically carried by a person.Specifically, the receiving device 24 in this embodiment may be a PDA, acellular phone (e.g., iPhone or Samsung Galaxy), a tablet (e.g., iPad orAndroid-based tablet), a laptop, a desktop computer, or a customdesigned device. For example, the receiving device 24 in this embodimentmay be a tablet that is positioned within the press box and can becarried into the locker room.

In the embodiment shown in FIG. 4, the receiving device 24 is acontroller 30 and physiological parameter data is transmitted from theIHU 22 to this receiving device 24 via communications link 52. As such,the receiving device 24 is not hand-held, but it may be portable in someinstances, and is typically positioned around (e.g., within 150 yards)the field or location that the physical activity is taking place. Whilethe receiving device 24 may be positioned around the field or location,the receiving device 24 in this embodiment is not carried by a person.Specifically, the receiving device 24 in this embodiment may be a tablet(e.g., iPad or Android-based tablet), a laptop, a desktop computer, or acustom designed device. For example, the receiving device 24 in thisembodiment may be a custom device that includes a laptop that is coupledto a receiving radio and is positioned on the sideline of the field.

In the embodiment shown in FIG. 5, the receiving device 24 includes botha network element 34 and a remote terminal 28. As such, alert data maybe nearly instantaneously transmitted to the alert unit 26 via awireless transmission over communications link 54, while all otherphysiological parameter data that is recorded by the IHU 22 may betransmitted via communications link 42 to the remote terminal 28 in anon-instantaneous fashion. For example, alert data associated with thesingle and cumulative impact alerts may be wirelessly transmitted fromthe IHU 22 to a network element 34 (e.g., Wi-Fi compatible router) thatis positioned proximate (e.g., within 50 yards) to the field or locationthat the physical activity is taking place. The network element 34(e.g., Wi-Fi compatible router) may then route the alert data associatedwith the single and cumulative impact alerts to the alert unit 26.Meanwhile, the IHU 22 will store all other recorded physiologicalparameter data within the IHU 22. Once the physical activity hasconcluded, the authorized user can download the physiological parameterdata from the IHU 22 using any wired or wireless technologies describedherein to the remote terminal 28 or directly to the team database 32.

In an embodiment shown in FIG. 6, the receiving device 24 is a networkelement 34. As such, all physiological parameter data recorded by theIHU 22 will be transmitted to the alert unit 26 via a wirelesstransmission over the network element 34. For example, all physiologicalparameter data may be wirelessly transmitted from the IHU 22 to anetwork element 34 (e.g., cellular network) via communications link 56.The network element 34 (e.g., cellular network) may then route thephysiological parameter data to the alert unit 26 via communicationslink 46. It should be understood that some of the data recorded by theIHU 22 may be transmitted in a nearly instantaneous fashion, while datarecorded by the IHU 22 may be transmitted in a non-instantaneousfashion.

a. Alert Unit

As described above, the alert unit 26 is hand-held, portable and istypically carried by a person that is: (i) positioned proximate (e.g.,within 50 yards) to the field or location that the physical activity istaking place and (ii) is not engaged in the physical activity (e.g.,sideline personnel, which may be a trainer). Specifically, the alertunit 26 in this embodiment may be a PDA, a cellular phone (e.g., iPhoneor Samsung Galaxy), a watch (e.g., iWatch or Android-based watch), atablet (e.g., iPad or Android-based tablet), or a custom designed devicespecifically designed to be a portable alert unit 26. For example, thealert unit 26 may be an iPhone or a custom designed device that iscarried within the trainer's pocket. In these exemplary embodiments, thealert unit 26 includes various components, such as a display, aprocessor, a memory, peripheral devices, a radio/transmitter/receiver,sensors, and other components.

In FIG. 1, the alert unit 26 receives physiological parameter data fromthe IHU 22, which includes alert data and impact magnitude datacontained within the impact matrixes from the IHU 22. The alert unit 26can process this physiological parameter data to display all of thealert data or a subset of the alert data to the sideline personnel(e.g., trainer) bearing the alert unit 26. In response to receivingalert data, the alert unit 26, shown in FIG. 1A, may display: (i) alerttype 26.2, (ii) impact time 26.4, and (iii) player's unique identifier(e g., name or jersey number) 26.6, 26.8. In other embodiments, thealert unit 26 may be configured to display all or a subset of thefollowing: (i) the impact value, (ii) impact location, (iii) impacttime, (iv) impact direction, (v) player's unique identifier, (vi) alerttype, (vii) player's heart rate, (viii) player's temperature, (ix)impact magnitude and/or (ix) other relevant information (e.g., activityinformation). The alert data may be displayed on the alert unit 26 in agraphical (e.g., on a representative image of a player) manner or in anon-graphical (e.g., numerical value) manner An alternative embodimentof the alert unit 26 is shown in FIG. 1B, which displays: (i) alert type26.2, (ii) impact time 26.4, (iii) player's unique identifier (e.g.,name or jersey number) 26.6, 26.8, (iv) number and type of alertsreceived of a predefined amount of time (e.g., 7 days) 26.10, (v) numberand type of training opportunities received of a predefined amount oftime (e.g., 7 days) 26.12, and a graphical representative image of aplayer showing the location of the impact that led to the single impactalert 26.14. Specifically, the user or administrator may set or selectwhich of the above data is displayed on the alert unit 26.

Once sideline personnel (e.g., trainer), who are typically not engagedin the physical activity, have received an alert on the alerting unit26, they can employ a method for evaluating and treating the player inquestion. Specifically, evaluating and treating the player in questionis described within U.S. Pat. No. 8,548,768, which is fully incorporatedherein by reference. For example, the alert unit 26 be programmed withinteractive software that assures best practices are followed in thetreatment and documentation of injuries, such as mild traumatic braininjuries (MTBI). The interactive software may include a bundle of teammanagement programs that enable the signaling device to store all teamdata, including medical histories and testing baselines. The interactivesoftware also provides the signaling device with an active responseprotocol for guiding sideline personnel through appropriate examinationprocedures and recording the results. For example, when the alert unit26 receives an alert and the relevant player is brought to the sidelinefor evaluation, the alert unit 26 can display the individual'shead-injury history, the results of previous evaluations and otherpertinent medical data. With the assistance of the interactive software,the signaling device prompts the medical staff member to conduct theappropriate sideline examination, records the responses, compares theresults to established baselines and prompts either further testing or aplay/no-play decision. The interactive software may also include asession manager program that allows the authorized user to documentincidents as they occur during a practice or a game. The appropriateinformation about the team, players and conditions is entered at thebeginning of each session. Then, as injuries occur, the interactivesoftware provides a template for recording injury data on a per-playerbasis. The data and results stored on the device can be uploaded to theteam database 32 wherein authorized users can access the same for teammanagement and player evaluation functions.

5. Team Database/National Database

FIG. 1 includes the team database 32, which stores all relevant teamdata. This team data may include, but is not limited to including,: (i)alert data for each player, which includes single and cumulative alertsfor each player, (ii) impact matrix for each player, (iii) other datarelated to the recorded physiological parameters for each player, (iv)setup associated with each IHU 22, (iv) practice calendars/schedules,(v) equipment assignments and profiles (e.g., relevant sizes, type ofshoes, type of helmet, type of helmet padding, type of chin strap, typeof faceguard, and etc.), (vi) medical data for each player (e.g.,medical histories, injuries, height, weight, emergency information, andetc.), (vii) statistics for each player, (viii) workout regiments foreach player, and (ix) other player data (e.g., head and helmet scans,contact information, class year). It should be understood that the teamdatabase 32 may contain data that has been generated by the team over asingle day or may have been collected over many years.

As shown in FIGS. 1, 4 and 5, the team database 32 is separate from: (i)the receiving device 24, (ii) the remote terminal 28, and (iii) thenational database 38. The team database 32 may be stored in a physicaldatabase located near where the team plays or on the team campus. Forexample, the team database 32 may be located within the athleticdepartment of a college or university, wherein the team database 32functions as an interactive clearinghouse or warehouse for all athleteinformation shared among various departments or sports. Alternatively,the team database 32 may be stored on a cloud server that is accessiblevia the internet.

In another embodiment shown in FIG. 3, the team database 32 may becombined with: (i) the receiving device 24 and (ii) the remote terminal28. This configuration may be simpler to implement in a smaller setting.Like system 10 described in FIGS. 1, 4 and 6, the national database 38is separate from the team database 32 in this embodiment. This allowsthe team database 32 to be stored locally on the remote terminal 28,which may allow an authorized user to access the team database 32without an internet connection. Once a predefined amount of time haspassed since the combination device shown in FIG. 3 has been connectedto the national database 38 via the internet, the device shown in FIG. 3will suggest that the user connect the combination device to theinternet to allow the device to: (i) pull updated thresholds from thenational database via communications link 60 and (ii) upload a copy ofthe team database 32 to the national database 38 via communications link60. In a further embodiment shown in FIG. 2, the team database 32 andthe national database 38 are combined into one database that is storedon a cloud server that is accessible via the internet. This embodimentmay be beneficial because it allows all data to be centrally located,which improves the functioning of the system 10 due to the fact itallows the system 10 perform the algorithms on the data without tryingto acquire this data from individual sources that may be unavailable ormay have a slow internet connection. Other benefits of combining thesedatabases includes reducing the requirements for the remote terminal 28,which in turn improves usability of the system because it allows greateraccess to the data. Other benefits of combing these databases are knownto one of skill in the art. Accordingly, in this embodiment, the remoteterminal 28 will connect to the combination of the team database 32 andthe national database 38 via the internet.

National database 38 stores all the data or a subset of the data that isstored in each of the team databases 32 around the nation or world.Specifically, the team databases 32 uploads a copy of the data to thenational database 38 via communications link 62 after a predefinedamount of time has passed since the team database 32 was last uploadedto the national database 38. Additionally, after the new data from theteam database 32 is uploaded to the national database 38, the teamdatabase 32 may download new thresholds from the national database 38via communications link 62. The data that may be contained within thenational database 38 may include, but is not limited to: (i) alert datafor each player across the nation/world, which includes single andcumulative alerts for each player across the nation/world, (ii) impactmatrix for each player across the nation/world, (iii) other data relatedto the recorded physiological parameters for each player across thenation/world, (iv) equipment assignments and profiles of each playeracross the nation/world (e.g., relevant sizes, type of shoes, type ofhelmet, type of helmet padding, type of chin strap, type of faceguard,and etc.), (v) medical data for each player across the nation/world(e.g., medical histories, injuries, height, weight, emergencyinformation, and etc.), (vi) statistics for each player across thenation/world, (vii) workout regiments for each player across thenation/world, and (viii) other player data across the nation/world(e.g., head and helmet scans, contact information). It should beunderstood that in certain embodiments a team may elect not tocontribute certain information from the team database 32 to the nationaldatabase 38.

6. Remote Terminal

The display 28 a of the remote terminal 28 permits an authorized user toview via the GUI, remotely or locally, the data contained within theteam/national database 32, 38 via communications link 60. This allowsthe authorized user to remotely keep abreast of changes in a player'sstatus or check to see if the team has equipment components to replacethe equipment that was lost or damaged during the physical activity. Forexample, the authorized user can view comparisons that may include: (i)recently recorded data contained within the team database 32 againsthistorical data contained within the team database 32, (ii) recentlyrecorded data contained within the team database 32 against historicaldata contained within the national database 38, and (iii) recentlyrecorded data contained within the team database 32 against recentlyrecorded data contained within the national database 38. Each comparisoncan provide information that can be used to make suggested changes inhow the player or team engages in physical activity. For example, if itis determined that the current quarterback for Team A is currentlyexperiencing more single alerts then other historical quarterbacks forTeam A, the authorized user may need to look at the playing style of thecurrent quarterback of Team A or may need to review the techniques ofhis lineman.

The remote terminal 28 may be an internet enabled device, such as aPDA,a cellular phone (e.g., iPhone or Samsung Galaxy), a tablet (e.g., iPador Android-based tablet), a laptop, a desktop computer, or a customdevice specifically designed to be a remote terminal. Unlike the alertunit 26, the remote terminal 28 is not specifically configured to becarried by a person that is: (i) positioned proximate (e.g., within 50yards) to the field or location that the physical activity is takingplace and (ii) is not engaged in the physical activity. While typicallybeing a portable device that can be transported from one location toanother location, the remote terminal 28 is typically configured to bepositioned in a trainer's office, the press box, locker room, home ofthe player or other similar locations. It should be understood that insome embodiments, the remote terminal 28 and the alert unit 26 may becombined into a single device, as shown in FIG. 3. Accordingly, in thisembodiment, the remote terminal 28 will be adapted to perform thefunctions of both the remote terminal 28 and the alert unit 26. Thus,this combination device will have the form factor that is similar to theform factor of the alerting unit 26. In these exemplary embodiments,remote terminal 28 includes various components, such as a display, aprocessor, a memory, peripheral devices, a radio/transmitter/receiver,sensors, and other components.

In certain embodiments, such as those shown in FIGS. 3 and 5, thephysiological parameter data is transmitted from the IHU 22 to theremote terminal 28 via communications link 42. It should be understoodthat these transmissions may include one or more network elements thatare positioned between the IHU 22 and the remote terminal 28. Thecommunications link 42 may use any type of wireless or wiredcommunication technology, examples of such technologies are discussedabove. In other embodiments, the physiological parameter data may bepass through at least one device (e.g., network element, such as arouter) prior to being received by the remote terminal 28. For example,in FIGS. 1 and 2, sometime after the physiological parameter data hasbeen sent from the IHU 22 to the receiving device 24, the receiving unit24 transmits the physiological parameter data to a remote terminal 28via communications link 44. Typically, the recorded data is transmittedfrom the receiving device 24 to the remote terminal 28 after theconclusion of the physical activity for that day. However, it should beunderstood that the recorded data may be transmitted more or lessfrequently depending on the physical activity. In still furtherembodiments, as shown in FIG. 4, the physiological parameter data may besent to the team database 32 via communications link 48 from controller30.

In any of the above embodiments, the team/national database 32, 38 maybe remotely accessed over the internet by an authorized user using theremote terminal 28. To ensure that the user is an authorized user, thesystem 10 requires that the user provide some type of information toidentify the user. For example, the remote terminal 28 may request aconnection with the team/national database 32, 38 from an internetenabled device. Once the team/national database 32, 38 receives thisrequest from the remote terminal 28, the team/national database 32, 38may ask the authorized user to enter their user name and password usingthe remote terminal 28. The user will then enter this information usingthe remote terminal 28 and the information will be sent to theteam/national database 32, 38 over the internet. The team/nationaldatabase 32, 38 then verifies this information by confirming the username and password that was provided matches the user name and passwordthat is stored within the team/national database 32, 38. Afterverification is complete, remote terminal 28 is given access, accordingto the authorized user's access level (e.g., admin, full access to teaminformation, only access to a single player, access to another subset ofthe data, etc.), to the information contained within the team/nationaldatabase 32, 38. It should be understood that other types of identifyinginformation (e.g., RSA tokens, information derived from the BIOS of acomputer, etc.) may be provided by the user in alternative embodiments.This allows the authorized user to remotely keep abreast of changes inthe player's status or check to see if the team has equipment componentsto replace an equipment component that was lost or damaged during thephysical activity.

It should be understood an authorized user who can view data containedwithin the team database 32 is not typically granted full access to viewdata contained within the national database 38. Typically, the onlyusers that have full access to this national database 38 are thedatabase administrators. This helps ensure that the data from theplayer's around the nation/world cannot be accessed by other authorizedusers. In certain embodiments, where the national database 38 and/orteam database 32 is cloud-based and is accessible via the internet, apartial authorized user (e.g., a parent) may have restricted access toan extent of the national database 38 and/or team database 32 may beaccessed by the remote terminal 28 via communications link 60. Forexample, this restricted access allows the partial authorized userand/or remote terminal 28 to view a specific player's recently recordedphysiological parameter data.

It should be understood that the disclosure contained herein discussesthat the remote terminal 28 performs the algorithms (e.g., 504, 506,508, 510, 512, 514, 516, and 518) discussed below and thus is performingthe comparison of the datasets (e.g., data contained within the teamdatabase 32 with data contained within the national database 38). In analternative embodiment, another device (e.g., remote cloud server) thatis connected to the team database 32 and the national database 38 mayperform these algorithms described below, while the remote terminal 28merely displays the outcomes of these algorithms in the GUI.

This alternative embodiment allows the remote terminal 28 to operatemore efficiently because it does not have to perform all of thesealgorithms, which also improves the efficiency of the system 10 as itreduces the amount of data that system 10 must simultaneously monitorand process. For example, the algorithms that are described above can beperformed during times when the system 10 is not being used (e.g., lateat night) during practice or game play, which in turn allows the system10 to focus on requests made by authorized users that are activelylogged into the system 10. Additionally, this alternative embodimentcentralizes the processing of the algorithms, which allows forspecialized devices to rapidly perform the algorithms Further,centralization of the algorithms reduce the cost of running the system10 because the system can tailor its power usage (e.g., when it performsthe above described algorithms) to select times when power is lessexpensive and by providing the reports the authorized user does not haveto log into the system to pull this information therefrom. Moreover,this centralization allows for the remote terminal 28 to have lessprocessing power and storage then would otherwise be required. Thisallows for the system 10 to provide access to a greater number ofauthorized users in a greater number of location; thus, improving theusability and efficiently of the system 10.

d. Training Opportunities

As discussed above, the system 10 provides post-physical activityanalysis of the recorded data to make suggested changes in how theperson engages in the physical activity using a unique set of rules oralgorithms Specifically, these suggestions are based on specifictraining opportunities.

i. Overview

Examples of training opportunities that the system 10 can determine areshown in FIG. 13A. Specifically, training opportunities may be based on:(i) comparing a specific or target player's recent physiologicalparameter data 320 against various collections of historicalphysiological parameter data 321 or (ii) comparing a specific or targetteam's recent physiological parameter data 322 against variouscollections of historical physiological parameter data 323. As will bediscussed in greater detail below, the following comparisons can bemade:

-   -   a. Comparing a specific or target player's recent physiological        parameter data against the player's own historical physiological        parameter data 324 will provide training opportunities that are        numbered 3, 5, and 7. This comparison may allow the specific or        target player to understand how they are currently performing in        comparison to their history.    -   b. Comparing a specific or target player's recent physiological        parameter data against the physiological parameter data from        other teammates that play the same or similar positions 326 will        provide training opportunities that are numbered 9-13. This        comparison may allow the specific or target player to understand        how they are currently performing in comparison to the history        of the player's teammates.    -   c. Comparing a specific or target player's recent physiological        parameter data against other player's physiological parameter        data, which have the same or similar playing level and position,        328 will provide training opportunities that are numbered 1, 2,        4, 6, and 8. This comparison may allow the specific or target        player to understand how they are currently performing in        comparison to the history of the player's around the        nation/world.    -   d. Comparing a specific or target team's recent physiological        parameter data against the team's own historical physiological        parameter data 330 will provide training opportunities that are        numbered 14-16. This comparison may allow the specific or target        team to understand how they are currently performing in        comparison to their history.    -   e. Comparing a specific or target team's physiological parameter        data against other team's physiological parameter data, which        have the same or similar playing level, 332 will provide        training opportunities that are numbered 17-21. This comparison        may allow the specific or target team to understand how they are        currently performing in comparison to the history of the        player's around the nation/world.

Examples of other training opportunities that the system 10 maydetermine are shown in FIGS. 13B-13E. Specifically, FIG. 13B discusseshow training opportunities may be based on: (i) comparing a specific ortarget player's recent physiological parameter data 320 against variouscollections of historical physiological parameter data 300 or (ii)comparing a specific or target player's recent physiological parameterdata 320 against various collections of recent physiological parameterdata 302. For example, the following comparisons can be made:

-   -   a. Comparing a specific or target player's recent physiological        parameter data against historical physiological parameter data        for players that: (i) are geographically local to the specific        or target player, (ii) have a playing level that is the same or        similar to the specific or target player, and (iii) play a        position that is the same or similar to the specific or target        player 306.    -   b. Comparing a specific or target player's recent physiological        parameter data against historical physiological parameter data        for players that: (i) are geographically regional to the        specific or target player, (ii) have a playing level the same or        similar to the specific or target player, and (iii) play a        position that is the same or similar to the specific or target        player 308.    -   c. Comparing a player's recent physiological parameter data        against recent physiological parameter data for players        that: (i) are geographically local to the specific or target        player, (ii) has a playing level that is the same or similar to        the specific or target player, and (iii) play a position that is        the same or similar to the specific or target player 312.    -   d. Comparing a player's recent physiological parameter data        against recent physiological parameter data for players        that: (i) are geographically regional to the player, (ii) have a        playing level similar to the player, and (iii) play a position        that is similar to the player 314.    -   e. Comparing a specific or target player's recent physiological        parameter data against recent physiological parameter data for        players that: (i) have playing level that is the same or similar        to the specific or target player, and (iii) play a position that        is the same or similar to the specific or target player 316.

Similarly, FIG. 13C discusses how training opportunities may be basedon: (i) comparing a specific or target position's (e.g., offensive line,running backs, quarterback, wide receivers, defensive linemen,linebackers, defensive backs and special teams) recent physiologicalparameter data 329 against various collections of historicalphysiological parameter data in 331 or (ii) comparing a specific ortarget position's recent physiological parameter data 329 againstvarious collections of recent physiological parameter data in 333. Forexample, the following comparisons can be made:

-   -   a. Comparing a specific or target position's recent        physiological parameter data against the position's own        historical physiological parameter data 334.    -   b. Comparing a specific or target position's recent        physiological parameter data against historical physiological        parameter data for positions that: (i) are geographically local        to the specific or target position and (ii) have a playing level        that is the same or similar to the specific or target position        335.    -   c. Comparing a specific or target position's recent        physiological parameter data against historical physiological        parameter data for positions that: (i) are geographically        regional to the specific or target position and (ii) have a        playing level that is same or similar to the specific or target        position 336.    -   d. Comparing a specific or target position's recent        physiological parameter data against historical physiological        parameter data for positions have a playing level that is the        same or similar to the specific or target position 337.    -   e. Comparing a specific or target position's recent        physiological parameter data against recent physiological        parameter data for positions that: (i) are geographically local        to the position and (ii) has a playing level the same or similar        to the specific or target position 338.    -   f. Comparing a specific or target position's recent        physiological parameter data against recent physiological        parameter data for positions that: (i) are geographically        regional to the position and (ii) have a playing level that is        the same or similar to the specific or target position 339.    -   g. Comparing a specific or target position's recent        physiological parameter data against recent physiological        parameter data for positions that have a playing level that is        the same or similar to the specific or target position 340.

Similarly, FIG. 13D discusses how training opportunities may be basedon: (i) comparing a specific or target unit's (e.g., offense, defense)recent physiological parameter data 342 against various collections ofhistorical physiological parameter data in 341 or (ii) comparing aspecific or target unit's recent physiological parameter data 342against various collections of recent physiological parameter data in343. For example, the following comparisons can be made:

-   -   a. Comparing a specific or target unit's recent physiological        parameter data against the unit's own historical physiological        parameter data 344.    -   b. Comparing a specific or target unit's recent physiological        parameter data against historical physiological parameter data        for units that: (i) are geographically local to the specific or        target unit, (ii) have a playing level that is the same or        similar to the specific or target unit, and (iii) include        positions that are the same or similar to the positions the        specific or target unit includes 346.    -   c. Comparing a specific or target unit's recent physiological        parameter data against historical physiological parameter data        for units that: (i) are geographically regional to the specific        or target unit, (ii) have a playing level that is the same or        similar to the unit, and (iii) include positions that are the        same or similar to the positions the specific or target unit        includes 348.    -   d. Comparing a specific or target unit's recent physiological        parameter data against historical physiological parameter data        for units that: (i) have playing level that is the same or        similar to the specific or target unit, and (iii) include        positions that are the same or similar to the positions the        specific or target unit includes 350.    -   e. Comparing a specific or target unit's recent physiological        parameter data against recent physiological parameter data for        units that: (i) are geographically local to the specific or        target unit, (ii) has a playing level that is the same or        similar to the unit, and (iii) include positions that are the        same or similar to the positions the specific or target unit        includes 352.    -   f. Comparing a specific or target unit's recent physiological        parameter data against recent physiological parameter data for        units that: (i) are geographically regional to the specific or        target unit, (ii) have a playing level that is the same or        similar to the unit, and (iii) include positions that are the        same or similar to the positions the specific or target unit        includes 354.    -   g. Comparing a specific or target unit's recent physiological        parameter data against recent physiological parameter data for        units that: (i) have playing level that is the same or similar        to the specific or target unit, and (iii) include positions that        are the same or similar to the positions the specific or target        unit includes 356.

Similarly, FIG. 13E discusses how training opportunities may be basedon: (i) comparing a specific or target team's recent physiologicalparameter data 322 against various collections of historicalphysiological parameter data in 371 or (ii) comparing a specific ortarget team's recent physiological parameter data 322 against variouscollections of recent physiological parameter data in 373. For example,the following comparisons can be made:

-   -   a. Comparing a specific or target team's recent physiological        parameter data against historical physiological parameter data        for teams that: (i) are geographically local to the specific or        target team and (ii) have a playing level that is the same or        similar to the specific or target team 370.    -   b. Comparing a specific or target team's recent physiological        parameter data against historical physiological parameter data        for teams that: (i) are geographically regional to the specific        or target team and (ii) have a playing level that is the same or        similar to the specific or target team 372.    -   c. Comparing a specific or target team's recent physiological        parameter data against recent physiological parameter data for        teams that: (i) are geographically local to the specific or        target team and (ii) has a playing level that is the same or        similar to the specific or target team 374.    -   d. Comparing a specific or target team's recent physiological        parameter data against recent physiological parameter data for        teams that: (i) are geographically regional to the specific or        target team, (ii) have a playing level that is the same or        similar to the specific or target team 376.    -   e. Comparing a specific or target team's recent physiological        parameter data against recent physiological parameter data for        teams that have playing level that is the same or similar to the        specific or target team 378.

As discussed above in connection with the IHU 22, certain thresholdvalues/ranges that are utilized by the algorithms are not standardizedacross all players. In other words, different or custom thresholdvalues/ranges may be utilized by the algorithms for each player. Also,like above, the non-standardized or custom threshold values/ranges arebased upon information that is entered or obtained from the player whosedata is going to be analyzed by the algorithm. In particular, tailoringthe algorithms to the specific player by using non-standardized orcustom threshold values/ranges creates a specialized multi-functionsystem 10 that determines training opportunities for the specificplayer. This specialized multi-function system 10 provides more accurateinformation, including monitoring and training opportunities to theauthorized user. This in turn improves the efficiency of the system 10and the authorized user's ability to make suggested changes in how theperson engages in the physical activity.

The non-standardized or custom threshold values/ranges in thisembodiment are: (i) 5^(th) threshold or number of alertable impactsthreshold, (ii) 6^(th) threshold or number of high magnitude impactsthreshold, (iii) 10^(th) threshold or number of impacts threshold, (iv)13^(th) threshold or over baseline average number of impacts threshold,(v) 14^(th) threshold or impact load threshold, (vi) 17^(th) thresholdor over baseline average load threshold, and (vii) 19^(th) threshold orlocation threshold. Similar to the above described process, system 10determines the values of these non-standardized or custom thresholdvalues/ranges based on the player's position and level. To select theproper thresholds/values, the remote terminal 28 pulls thethresholds/values from the team/national database 32, 38 that wereassociated with the player during the setup of the IHU 22. Also, asdescribed above, these non-standardized threshold values/ranges may beadjusted or a different set of thresholds/values may be selected in amanner that is similar to the above described processes. It should beunderstood that additional threshold values/ranges may be added to theabove disclosed list of threshold values/ranges or thresholdvalues/ranges may be subtracted.

2. Algorithms for Training Opportunity

FIGS. 14-18 disclose eight novel training opportunity algorithms 504,506, 508, 510, 512, 514, 516, and 518. These novel training opportunityalgorithms each create multiple training opportunities depending on thedata that is compared within the algorithm. For example, one algorithmmay compare a specific player's recent physiological parameter data witha specific player's historical physiological parameter data.Additionally, the same algorithm may compare a specific or target team'srecent physiological parameter data with a specific team's historicalphysiological parameter data. It should be understood that while a fewtraining opportunities for each algorithm are discussed below,additional training opportunities for each of these algorithms exist andare contemplated by this disclosure.

Training opportunity algorithm 504 is shown in FIG. 14 and generallyrelates to a high number of alertable impacts. For example, thealertable impacts are the impacts that the specific player experiencedthat are greater than the 3^(rd) or 4^(th) thresholds, as described inFIG. 12. In other words, only impacts that generate either a singlealert or a cumulative alert are analyzed by algorithm 504. The 1^(st)training opportunity or the high number of alertable impacts for aspecific player v. nation training opportunity may be generallydetermined by comparing a value that is derived from a specific player'salert data with a value that is derived from the national alert data. Inother words, the 1^(st) training opportunity may be generally determinedby comparing a specific player value derived from data contained withinthe team database 32 with a value derived from data contained within thenational database 38 using algorithm 504. Specifically, values and dataused in this 15^(th) training opportunity include:

-   -   a. Training Opportunity #1 or High Number of Alertable Impacts        for Specific Player v. National Player        -   i. Comparison: a specific player's number of alertable            impacts vs. historical number of alertable impacts            experienced by similarly situated players in terms of            playing level and/or position;        -   ii. Data Requirement: (i) number of alertable impacts over a            2^(nd) predefined amount of time for the specific player            and (ii) number of alertable impacts over the 2^(nd)            predefined amount of time for similarly situated players in            terms of playing level and/or position;        -   iii. 2^(nd) Predefined Amount of Time or Alertable Time            Period: set to any amount of time, preferably set between 2            days and 90 days, and most preferably set to 7 days; and        -   iv. 5^(th) Threshold or Number of Alertable Impacts            Threshold: set to the 95^(th) percentile of the number of            alertable impacts that have historically occurred over the            2^(nd) predefined amount of time for similarly situated            players in terms of playing level and/or position.

Specifically, the remote terminal 28 performs the steps described inalgorithm 504. First, in step 560, the remote terminal 28 sums up thetotal number of single and cumulative impacts that the specific playerexperienced over a 2^(nd) predefined time period or an alertable timeperiod. The 2^(nd) predefined time period or an alertable time periodmay be set to any amount of time, preferably set between 2 days and 90days, and most preferably set to 7 days. Once the remote terminal 28 hasdetermined the total number of alertable impacts experienced by thespecific player over the alertable time period in step 560, this totalnumber is compared with a 5th threshold or a number of alertable impactsthreshold in step 562. The number of alertable impacts threshold may beset to the 95^(th) percentile of the number of alertable events thathave historically occurred over the alertable time period for similarlysituated players in terms of playing level and/or position. If the totalnumber of alertable impacts over the alertable time period is less thanthe number of alertable impacts threshold, then remote terminal 28performs no additional steps. However, if the total number of alertableimpacts over the alertable time period is greater than the number ofalertable impacts threshold, then the 15^(th) training opportunity istriggered in step 564. This 15^(th) training opportunity informs anauthorized user that the specific player is experiencing more alertsthan other similarly situated players in terms of playing level and/orposition. Accordingly, the authorized user should review the game tapewith this player at the timestamps that each alertable incident occurredto determine how the specific player may change their playing style toreduce the number of alertable impacts in the future.

Training opportunity algorithm 504 may be used to generate anothertraining opportunity by altering the data the algorithm 504 compares.Instead of comparing a specific player's data to national data, thetraining opportunity algorithm 504 may compare a specific player's datato team data. The 9^(th) training opportunity or the high number ofalertable impacts for specific player v. team training opportunity maybe generally determined by comparing a value that is derived from aspecific player's alert data with a value that is derived from theteam's alert data. In other words, the 9^(th) training opportunity maybe generally determined by comparing a specific player value derivedfrom data contained within the team database 32 with a team valuederived from data contained within the team database 32 using algorithm504. Specifically, values and data used in this 9^(th) trainingopportunity include:

-   -   a. Training Opportunity #9 or High Number of Alertable Impacts        for Specific Player v. Team        -   i. Comparison: specific player's number of alertable impacts            vs. historical number of alertable impacts experienced by            teammates that play similar positions;        -   ii. Data Requirement: (i) number of alertable impacts over a            2^(nd) predefined amount of time for the specific player            and (ii) number of alertable impacts over the 2^(nd)            predefined amount of time for teammates that play similar            positions;        -   iii. 2^(nd) Predefined Amount of Time or Alertable Time            Period: set to any amount of time, preferably set between 2            days and 90 days, and most preferably set to 7 days; and        -   iv. 5^(th) Threshold or Number of Alertable Impacts            Threshold: set to the 95^(th) percentile of the number of            alertable events that have historically occurred over the            2^(nd) predefined amount of time for teammates that play            similar positions.

This 9^(th) training opportunity uses the same data flow that isdescribed above in connection with the 1^(st) training opportunity,except for the fact that it utilizes different data sets. This 9^(th)training opportunity informs an authorized user that the specific playeris experiencing more alertable impacts than other team players that playthe same or similar positions. Accordingly, the authorized user shouldreview the game tape at the alertable time periods with this specificplayer to determine how to correct their playing style to match otherplayers that play the same or similar positions.

Training opportunity algorithm 504 may further be used to generateanother training opportunity by altering the data the algorithm 504compares. Instead of comparing a specific player's data to nationaldata, the training opportunity algorithm 504 compares a team's data tonational team data. The 17^(th) training opportunity or the high numberof alertable impacts for team v. national team data training opportunitymay be generally determined by comparing a value that is derived from aspecific team's alert data with a value that is derived from nationalalert data. In other words, the 17^(th) training opportunity may begenerally determined by comparing a specific team value derived fromdata contained within the team database 32 with a value derived fromdata contained within the national database 38 using algorithm 504.Specifically, values and data used in this 17^(th) training opportunityinclude:

-   -   a. Training Opportunity #17 or High Number of Alertable Impacts        for Team v. National Team        -   i. Comparison: specific team's number of alertable impacts            vs. historical number of alertable impacts for teams of            similar playing level;        -   ii. Data Requirement: (i) number of alertable impacts over a            2^(nd) predefined amount of time for the specific team            and (ii) number of alertable impacts over the 2^(nd)            predefined amount of time for all teams of similar playing            level;        -   iii. 2^(nd) Predefined Amount of Time or Alertable Time            Period: set to any amount of time, preferably set between 2            days and 90 days, and most preferably set to 7 days; and        -   iv. 5^(th) Threshold or Number of Alertable Impacts            Threshold: set to the 95^(th) percentile of the number of            alertable events that have historically occurred over the            2^(nd) predefined amount of time for teams of similar            playing levels.

This 17^(th) training opportunity uses the same data flow that isdescribed above in connection with the 1^(st) training opportunity,except for the fact that it utilizes different data sets. This 17^(th)training opportunity informs an authorized user that the specific teamis experiencing more alerts than other teams that play at a similarlevel. Accordingly, a wholesome review of the specific teams playingstyle should be reviewed.

Training opportunity algorithm 506 is shown in FIG. 14 and generallyrelates to a high number of high magnitude impacts. For example, highmagnitude impacts are the impacts that are greater than the 2^(nd)threshold, as described in FIG. 12. In other words, high magnitudeimpacts are impacts that are greater than the 95 percentile forsimilarly situated players in terms of playing level and/or position.The 2^(nd) training opportunity or the high number of high magnitudeimpacts for specific player v. nation training opportunity may begenerally determined by comparing a value that is derived from aspecific player's recorded impact values with a value that is derivedfrom the national recorded impact values. In other words, the 2^(nd)training opportunity may be generally determined by comparing thespecific player value derived from data contained within the teamdatabase 32 with a value derived from data contained within the nationaldatabase 38 using algorithm 506. Specifically, values and data used inthis 2^(nd) training opportunity include:

-   -   a. Training Opportunity #2 or High Number of High Magnitude        Impacts for Specific Player v. National Player        -   i. Comparison: specific player's number of high magnitude            impacts vs. historical number of high magnitude impacts            experienced by similarly situated players in terms of            playing level and/or position;        -   ii. Data Requirement: (i) number of high magnitude impacts            over a 3^(rd) predefined amount of time for the specific            player and (ii) number of high magnitude impacts over the            3^(rd) predefined amount of time for similarly situated            players in terms of playing level and/or position;        -   iii. 3^(rd) Predefined Amount of Time or High Magnitude Time            Period: set to any amount of time, preferably set between 2            days and 90 days, and most preferably set to 7 days;        -   iv. 2^(nd) Threshold or High Magnitude Impact Threshold: set            to the 95^(th) percentile of impacts recorded by similarly            situated players in terms of playing level and/or position;            and        -   v. 6^(th) Threshold or Number of High Magnitude Impacts            Threshold: set to the 95^(th) percentile of the number of            high magnitude impacts that have historically occurred over            the 3^(rd) predefined amount of time for similarly situated            players in terms of playing level and/or position.

Specifically, remote terminal 28 performs the steps described inalgorithm 506. First, in step 566, the remote terminal 28 calculates animpact value for each impact that is contained within the specificplayer's physiological parameter data over the 3^(rd) predefined amountof time or the high magnitude time period. These impact values may becalculated using any of the methods discussed above in connection withFIG. 12. For example, the impact values may be based on HITsp. The3^(rd) predefined amount of time may be set to any amount of time,preferably set between 2 days and 90 days, and most preferably set to 7days. Next, in step 568, the remote terminal 28 determines if thespecific player has experienced an impact that is over the 2^(nd)threshold or high magnitude impact threshold. The high magnitude impactthreshold may be set to the 95^(th) percentile of impacts recorded bysimilarly situated players in terms of playing level and/or position. Ifthe specific player has not experienced a high magnitude impact duringthe high magnitude time period, then the remote terminal 28 performs noadditional steps. However, if the specific player has experienced a highmagnitude impact during the high magnitude time period, then, in step570, the remote terminal 28 determines the number of high magnitudeimpacts the specific player experienced during the high magnitude timeperiod.

Once the remote terminal 28 has determined the total number of highmagnitude impacts experienced by the specific player over the highmagnitude time period in step 570, this total number is compared with a6^(th) threshold or a number of high magnitude impacts threshold in step572. The number of high magnitude impacts threshold may be set to the95^(th) percentile of the number of high magnitude impacts that havehistorically occurred over the high magnitude time period for similarlysituated players in terms of playing level and/or position. If thespecific player's total number of high magnitude impacts over the highmagnitude time period is less than the number of high magnitude impactsthreshold, then the remote terminal 28 performs no additional steps.However, if the specific player's total number of high magnitude impactsover the high magnitude time period is greater than the number of highmagnitude impacts threshold, then the 2^(nd) training opportunity istriggered in step 574. This 2^(nd) training opportunity informs anauthorized user that the specific player is experiencing more highmagnitude impacts than similarly situated players in terms of playinglevel and/or position. Accordingly, the authorized user should reviewthe game tape with this specific player to determine how the player maychange their playing style to avoid these high magnitude impacts in thefuture.

Training opportunity algorithm 506 may also be used to generate anothertraining opportunity by altering the data the algorithm 506 compares.Instead of comparing a specific player's data to national data, thetraining opportunity algorithm 506 may compare a specific player's datato team data. The 10^(th) training opportunity or the high number ofhigh magnitude impacts for specific player v. team training opportunitymay be generally determined by comparing a value that is derived from aspecific player's recorded impact values with a value that is derivedfrom the team's recorded impact values. In other words, the 10^(th)training opportunity may be generally determined by comparing a specificplayer value derived from data contained within the team database 32with a team value derived from data contained within the team database32 using algorithm 506. Specifically, values and data used in this10^(th) training opportunity include:

-   -   a. Training Opportunity #10 or High Number of High Magnitude        Impacts for Specific Player v. Team        -   i. Comparison: specific player's number of high magnitude            impacts vs. historical number of high magnitude impacts            experienced by teammates that play similar positions;        -   ii. Data Requirement: (i) number of high magnitude impacts            over a 3^(rd) predefined amount of time for the specific            player and (ii) number of high magnitude impacts over the            3^(rd) predefined amount of time for teammates that play            similar positions;        -   iii. 3^(rd) Predefined Amount of Time or High Magnitude Time            Period: set to any amount of time, preferably set between 2            days and 90 days, and most preferably set to 7 days;        -   iv. 2^(nd) Threshold or High Magnitude Impact Threshold: set            to the 95^(th) percentile of impacts recorded by similarly            situated players in terms of playing level and/or position;            and        -   v. 6^(th) Threshold or Number of High Magnitude Impacts            Threshold: set to the 95^(th) percentile of the number of            high magnitude impacts that have historically occurred over            the 3^(rd) predefined amount of time for teammates that play            similar positions.

This 10^(th) training opportunity uses the same data flow that isdescribed above in connection with the 2^(nd) training opportunity,except for the fact that it utilizes different data sets. It should beunderstood that instead of using all players of similar player level andposition to determine the 2^(nd) threshold, this 2^(nd) threshold may bebased on teammates that play similar positions. This 10^(th) trainingopportunity informs an authorized user that the specific player isexperiencing more high magnitude impacts than other team players thatplay the same or similar positions. Accordingly, the authorized usershould review the game tape with this specific player to determine howto correct their playing style to match other players that play similarpositions.

Training opportunity algorithm 506 may be used to generate anothertraining opportunity by altering the data the algorithm 506 compares.Instead of comparing a specific player's data to national data, thetraining opportunity algorithm 506 may compare a specific team's data tonational data. The 18^(th) training opportunity or the high number ofhigh magnitude impacts for specific team v. nation training opportunitymay be generally determined by comparing a value that is derived from ateam's recorded impact values with a value that is derived from thenational recorded impact values. In other words, the 18^(th) trainingopportunity may be generally determined by comparing a specific teamvalue derived from data contained within the team database 32 with avalue derived from data contained within the national database 38 usingalgorithm 506. Specifically, values and data used in this 18^(th)training opportunity are described below:

-   -   a. Training Opportunity #18 or High Number of High Magnitude        Impacts for Specific Team v. National Team        -   i. Comparison: specific team's number of high magnitude            impacts vs. historical number of high magnitude impacts            experienced by all teams of similar playing level;        -   ii. Data Requirement: (i) number of high magnitude impacts            over a 3^(rd) predefined amount of time for the specific            team and (ii) number of high magnitude impacts over the            3^(rd) predefined amount of time for all teams of similar            playing level;        -   iii. 3^(rd) Predefined Amount of Time or High Magnitude Time            Period: set to any amount of time, preferably set between 2            days and 90 days, and most preferably set to 7 days;        -   iv. 2^(nd) Threshold or High Magnitude Impact Threshold: set            to the 95^(th) percentile of impacts recorded by similarly            situated players in terms of playing level and/or position;            and        -   v. 6^(th) Threshold or Number of High Magnitude Impacts            Threshold: set to the 95^(th) percentile of the number of            high magnitude impacts that have historically occurred over            the 3^(rd) predefined amount of time for teams of similar            playing level.

This 18^(th) training opportunity uses the same data flow that isdescribed above in connection with the 2^(nd) training opportunityexcept for the fact that it utilizes different data sets. This 18^(th)training opportunity informs an authorized user that the specific teamis experiencing more high magnitude impacts than other teams that playat a similar playing level. Accordingly, a wholesome review of thespecific teams playing style should be review.

Training opportunity algorithm 508 is shown in FIG. 15 and generallyrelates to an increase in the number of high magnitude impacts. Forexample, high magnitude impacts are the impacts that the specific playerexperiences that are greater than the 2^(nd) threshold, as described inFIG. 12. In other words, high magnitude impacts may be impacts that aregreater than the 95^(th) percentile for impacts recorded by similarlysituated players in terms of playing level and/or position. The 3^(rd)training opportunity or the increased number of high magnitude impactsfor specific player v. specific player's history training opportunitymay be generally determined by comparing a value that is derived from aspecific player's recently recorded impact values with a value that isderived from a specific player's historical recorded impact values.Specifically, values and data used in this 3^(rd) training opportunityinclude:

-   -   a. Training Opportunity #3 or Increased Number of High Magnitude        Impacts for Specific Player v. Specific Player's History        -   i. Comparison: specific player's recent number of high            magnitude impacts vs. specific player's historical number of            high magnitude impacts;        -   ii. Data Requirement: (i) number of recent high magnitude            impacts over a 4^(th) predefined amount of time for the            specific player and (ii) historical number of high magnitude            impacts over the 4^(th) predefined amount of time for the            specific player;        -   iii. 4^(th) Predefined Amount of Time or High Magnitude            Frequency Time Period: set between 2 days and 90 days,            preferably set between 2 days and 30 days, and most            preferably set to 7 days;        -   iv. 2^(nd) Threshold or High Magnitude Impact Threshold: set            to the 95^(th) percentile of impacts recorded by similarly            situated players in terms of playing level and/or position;        -   v. 7^(th) Threshold or Historical High Magnitude Impact            Threshold: set to require at least 2 datasets, preferably at            least 5 datasets, and most preferably at least 10 datasets;        -   vi. 8^(th) Threshold or Baseline Number of High Magnitude            Impact Threshold: set above 0.1 average number of high            magnitude impacts, preferably above 0.05 average number of            high magnitude impacts, and most preferably above 1 average            number of high magnitude impacts; and        -   vii. 9^(th) Threshold or Over Baseline Average Number Of            High Magnitude Impacts

Threshold: set to 1% over the baseline average number of high magnitudeimpacts, which was calculated in step 584, preferably set between 5% and50% over the baseline average number of high magnitude impacts, whichwas calculated in step 584, and most preferably between 10% and 40% overthe baseline average number of high magnitude impacts, which wascalculated in step 584.

Specifically, remote terminal 28 performs the steps described inalgorithm 508. Also, FIG. 22 provides an example of how the specificplayer's physiological parameter data may be analyzed by algorithm 508.First, in step 576, the remote terminal 28 calculates an impact valuefor each impact contained within the specific player's physiologicalparameter data. These impact values may be calculated using any of themethods discussed above in connection with FIG. 12. For example, theimpact values may be based upon HITsp. Next, in step 578, the remoteterminal 28 determines if the specific player has experienced an impactthat is over the 2^(nd) threshold or high magnitude impact threshold.The high magnitude impact threshold may be set to the 95^(th) percentileof impacts recorded by similarly situated players in terms of playinglevel and/or position. If the specific player has not experienced a highmagnitude impact, then the remote terminal 28 performs no additionalsteps. However, if the specific player has experienced a high magnitudeimpact, then, in step 580, the remote terminal 28 determines the averagehistorical number of high magnitude impacts over each 4^(th) predefinedtime period or high magnitude frequency time period contained within thespecific player's physiological parameter data. Specifically, the remoteterminal 28 groups the high magnitude impacts into datasets based on thedate they occurred, wherein the dataset boundaries are defined by the4^(th) predefined amount of time. The 4^(th) predefined amount of timeis set between 2 days and 90 days, preferably set between 2 days and 30days, and most preferably set to 7 days. Once the high magnitude impactshave been grouped into these datasets, the remote terminal 28 averagesthe number of high magnitude impacts that occurred within each of thesedatasets.

Next, in step 582, the remote terminal 28 determines if the specificplayer's physiological parameter data contains enough high magnitudeimpacts to perform the calculations involved with this trainingopportunity. This helps ensure that this training opportunity is notunnecessarily suggested when there is not enough data for this trainingopportunity to be accurately presented to the authorized user. The7^(th) threshold or historical high magnitude impact threshold may beset to require at least 2 datasets, preferably at least 5 datasets, andmost preferably at least 10 datasets. If the specific player has notplayed long enough to record data over the required number of historicalhigh magnitude impacts threshold, then the remote terminal 28 performsno additional steps. However, if the specific player has recorded dataover the required number of historical high magnitude impacts, then, instep 584, the remote terminal 28 determines the baseline average numberof high magnitude impacts for the specific player.

Next, in step 586, the remote terminal 28 compares the baseline averagenumber of high magnitude impacts to an 8^(th) threshold or a baselinenumber of high magnitude impacts threshold. The 8^(th) threshold may beset above 0.1 average number of high magnitude impacts, preferably above0.05 average number of high magnitude impacts, and most preferably above1 average number of high magnitude impacts. If the specific player'sbaseline average number of high magnitude impacts is not over thebaseline number of high magnitude impacts threshold, then the remoteterminal 28 performs no additional steps. However, if the specificplayer's baseline average number of high magnitude impacts is over thebaseline number of high magnitude impacts threshold, then, in step 588,the remote terminal 28 determines the recent average number of highimpacts. The remote terminal 28 then compares the recent average numberof high impacts against the 9^(th) threshold or the over baselineaverage number of high magnitude impacts threshold in step 590. The9^(th) threshold may be set to 1% over the baseline average number ofhigh magnitude impacts, which was calculated in step 584, preferably setbetween 5% and 50% over the baseline average number of high magnitudeimpacts and most preferably between 10% and 40% over the baselineaverage number of high magnitude impacts. If the recent average numberof high magnitude impacts is not over the 9^(th) threshold, then theremote terminal 28 performs no additional steps. However, if the recentaverage number of high magnitude impacts is over the 9^(th) threshold,then the training opportunity is triggered in step 592. This 3^(rd)training opportunity informs an authorized user that the specific playeris experiencing, on average, more high magnitude impacts than thespecific player has previously experienced. Accordingly, the authorizeduser should review the specific player's form to see what has recentlychanged with the specific player. For example, did the specific playerrecently come back from an injury and is now favoring that side, whichis causing the specific player to have additional high magnitudeimpacts.

Training opportunity algorithm 508 may be used to generate anothertraining opportunity by altering the data the algorithm 508 compares.Instead of comparing a specific player's recent data to the specificplayer's historical data, the training opportunity algorithm 508 maycompare a specific team's recent data to the specific team's historicaldata. The 15^(th) training opportunity or the increased number of highmagnitude impacts for specific team v. specific team's history trainingopportunity may be generally determined by comparing a value that isderived from a specific team's recent recorded impact values with avalue that is derived from the specific team's historical impact values.Specifically, values and data used in this 15^(th) training opportunityare described below:

-   -   a. Training Opportunity #3 or Increased Number of High Magnitude        Impacts for Specific Team v. Team's History        -   i. Comparison: specific team's recent number of high            magnitude impacts vs. specific team's historical number of            high magnitude impacts;        -   ii. Data Requirement: (i) number of recent high magnitude            impacts over a 4^(th) predefined amount of time for the            specific team and (ii) historical number of high magnitude            impacts over the 4^(th) predefined amount of time for the            specific team;        -   iii. 2^(nd) Threshold or High Magnitude Impact Threshold:            set to the 95^(th) percentile of impacts recorded by            similarly situated players in terms of playing level and/or            position;        -   iv. 4^(th) Predefined Amount of Time or High Magnitude            Frequency Time Period: set between 2 days and 90 days,            preferably set between 2 days and 30 days, and most            preferably set to 7 days.        -   v. 7^(th) Threshold or Historical High Impact Threshold: set            to require at least 2 datasets, preferably at least 5            datasets, and most preferably at least 10 datasets;        -   vi. 8^(th) Threshold or Baseline Number of High Magnitude            Impact Threshold: set above 0.1 average number of high            magnitude impacts, preferably above 0.05 average number of            high magnitude impacts, and most preferably above 1 average            number of high magnitude impacts;        -   vii. 9^(th) Threshold or Over Baseline Average Number Of            High Magnitude Impacts

Threshold: set to 1% over the baseline average number of high magnitudeimpacts, which was calculated in step 584, preferably set between 5% and50% over the baseline average number of high magnitude impacts, whichwas calculated in step 584, and most preferably between 10% and 40% overthe baseline average number of high magnitude impacts, which wascalculated in step 584; and

This 15^(th) training opportunity uses the same data flow that isdescribed above in connection with the 3^(rd) training opportunityexcept for the fact that it utilizes different data sets. This 15^(th)training opportunity informs an authorized user that the specific teamis experiencing an increase in high magnitude impacts in comparison tothe specific team's history. Thus, a review of the recent drills thatthe team is performing or other changes in coaching style should bereviewed.

Training opportunity algorithm 510 is shown in FIG. 16 and generallyrelates to a high number of impacts. The 4^(th) training opportunity orthe high number of impacts for specific player v. nation trainingopportunity may be generally determined by comparing a value that isderived from a specific player's physiological parameter data with avalue that is derived from the national physiological parameter data. Inother words, the 4^(th) training opportunity may be generally determinedby comparing a specific player value derived from data contained withinthe team database 32 with a value derived from data contained within thenational database 38 using algorithm 510. Specifically, values and dataused in this 4^(th) training opportunity include:

-   -   a. Training Opportunity #4 or High Number of Impacts for        Specific Player v. National Player        -   i. Comparison: specific player's number of impacts vs.            historical number of impacts experienced by similarly            situated players in terms of playing level and/or position;        -   ii. Data Requirement: (i) number of impacts over a 5^(th)            predefined amount of time for the specific player and (ii)            number of impacts over the 5^(th) predefined amount of time            for similarly situated players in terms of playing level            and/or position;        -   iii. 5^(th) Predefined Amount of Time or Impact Time Period:            set to any amount of time, preferably set between 2 days and            90 days, and most preferably set to 7 days; and        -   iv. 10^(th) Threshold or Number of Impacts Threshold: set to            the 95^(th) percentile of the number of impacts that have            historically occurred over the 5^(th) predefined amount of            time for similarly situated players in terms of playing            level and/or position.

Specifically, the remote terminal 28 performs the steps described inalgorithm 510. First, in step 594, the remote terminal 28 sums up thetotal number of impacts that the specific player experienced over a5^(th) predefined time period or an impact time period. The 5^(th)predefined time period may be set to any amount of time, preferably setbetween 2 days and 90 days, and most preferably set to 7 days.Specifically, this is done by adding together every impact matrixcontained within the 5^(th) predefined time period to generate a summedimpact matrix. An example of how matrixes can be added together is shownin FIG. 21. Once all impact matrixes are added together over the 5^(th)predefined time period, each entry in the summed impact matrix is addedtogether. For example, this final number of impacts is equal to 13 forthe example shown in FIG. 21.

Once the remote terminal 28 has determined the total number of impactsexperienced by the specific player over the impact time period in step594, this total number is compared with a 10^(th) threshold or a numberof impacts threshold in step 596. The number of impacts threshold may beset to the 95^(th) percentile of the number of impacts that havehistorically occurred over the impact time period for similarly situatedplayers in terms of playing level and/or position. If the total numberof impacts over the impact time period is less than the number ofimpacts threshold, then remote terminal 28 performs no additional steps.However, if the total number of impacts over the impacts time period isgreater than the number of impacts threshold, then the 4th trainingopportunity is triggered in step 598. This 4th training opportunityinforms an authorized user that the specific player is experiencing moreimpacts than other similarly situated players in terms of playing leveland/or position.

As described above, training opportunity algorithm 510 may be used togenerate another training opportunity by altering the data the algorithm510 compares. Instead of comparing a specific player's data to nationaldata, the training opportunity algorithm 510 may compare a specificplayer's data to team data. The 11^(th) training opportunity or the highnumber of impacts for specific player v. team training opportunity maybe generally determined by comparing a value that is derived from aspecific player's physiological parameter data with a value that isderived from the team's physiological parameter data. In other words,the 11^(th) training opportunity may be generally determined bycomparing a specific player value derived from data contained within theteam database 32 with a team value derived from data contained withinthe team database 32 using algorithm 510. Specifically, values and dataused in this 10^(th) training opportunity include:

-   -   a. Training Opportunity #10 or High Number of Impacts for        Specific Player v. Team        -   i. Comparison: specific player's number of impacts vs.            historical number of impacts experienced by teammates that            play similar positions;        -   ii. Data Requirement: (i) number of impacts over a 5^(th)            predefined amount of time for the specific player and (ii)            number of impacts over the 5^(th) predefined amount of time            for teammates that play similar positions;        -   iii. 5^(th) Predefined Amount of Time or Impact Time Period:            set to any amount of time, preferably set between 2 days and            90 days, and most preferably set to 7 days; and        -   iv. 10^(th) Threshold or Number of Impacts Threshold: set to            the 95^(th) percentile of the number of impacts that have            historically occurred over the 5^(th) predefined amount of            time for teammates that play similar positions.

This 11^(th) training opportunity uses the same data flow that isdescribed above in connection with the 4^(th) training opportunity,except for the fact that it utilizes different data sets. This 11^(th)training opportunity informs an authorized user that the specific playeris experiencing more impacts than other team players that play similarpositions. This training opportunity may inform the authorized user thatthe individual specific player's playing style needs to be reviewedbecause they are experiencing impacts that are different than theirteammates.

Training opportunity algorithm 510 may be used to generate anothertraining opportunity by altering the data the algorithm 510 compares.Instead of comparing a specific player's data to national data, thetraining opportunity algorithm 510 compares a team's data to nationalteam data. The 19^(th) training opportunity or the high number ofimpacts for team v. national training opportunity may be generallydetermined by comparing a value that is derived from a team'sphysiological parameter data with a value that is derived from nationalphysiological parameter data. In other words, the 19^(th) trainingopportunity may be generally determined by comparing a specific teamvalue derived from data contained within the team database 32 with avalue derived from data contained within the national database 38 usingalgorithm 504. Specifically, values and data used in this 19^(th)training opportunity include:

-   -   a. Training Opportunity #19 or High Number of Impacts for        Specific Team v. National Team        -   i. Comparison: specific team's number of impacts vs.            historical number of impacts for teams of similar playing            level;        -   ii. Data Requirement: (i) number of impacts over a 5^(th)            predefined amount of time for the teams and (ii) number of            impacts over the 5^(th) predefined amount of time for all            teams of similar playing level;        -   iii. 5^(th) Predefined Amount of Time or Impact Time Period:            set to any amount of time, preferably set between 2 days and            90 days, and most preferably set to 7 days; and        -   iv. 10^(th) Threshold or Number of Impacts Threshold: set to            the 95^(th) percentile of the number of impacts that have            historically occurred over the 5^(th) predefined amount of            time for teams of similar playing level.

This 19^(th) training opportunity uses the same data flow that isdescribed above in connection with the 4^(th) training opportunity,except for the fact that it utilizes different data sets. This 19^(th)training opportunity informs an authorized user that the specific teamis experiencing more impacts than other teams that play at a similarlevel. Accordingly, a wholesome review of the specific teams playingstyle should be reviewed.

Training opportunity algorithm 512 is shown in FIG. 16 and generallyrelates to an increase in the number of impacts. The 5^(th) trainingopportunity or the increased number of impacts for specific player v.specific player's history training opportunity may be generallydetermined by comparing a value that is derived from a specific player'srecently recorded physiological parameter data with a value that isderived from a specific player's historical recorded physiologicalparameter data. Specifically, values and data used in this 5^(th)training opportunity include:

-   -   -   a. Training Opportunity #5 or Increased Number of Impacts            for Specific Player v. Specific Player's History        -   i. Comparison: specific player's recent number of impacts            vs. specific player's historical number of impacts;        -   ii. Data Requirement: (i) number of recent impacts over a            6^(th) predefined amount of time for the specific player            and (ii) historical number of impacts over the 6^(th)            predefined amount of time for the specific player;        -   iii. 6^(th) Predefined Amount of Time or Impact Frequency            Time Period: set between 2 days and 90 days, preferably set            between 2 days and 30 days, and most preferably set to 7            days.        -   iv. 11^(th) Threshold or Historical Impact Threshold: set to            require at least 2 datasets, preferably at least 5 datasets,            and most preferably at least 10 datasets;        -   v. 12^(th) Threshold or Baseline Impact Threshold: set above            0.1 average number of impacts, preferably above 8 average            number of impacts, and most preferably above 15 average            number of impacts; and        -   vi. 13^(th) Threshold or Over Baseline Average Number of            Impacts Threshold: set to 95^(th) percentile of change of            players of similar skill level.

Specifically, the remote terminal 28 performs the steps described inalgorithm 512. Also, FIG. 23 provides an example of how the specificplayer's physiological parameter data may be analyzed by algorithm 512.First, in step 600, the remote terminal 28 determines the averagehistorical number of impacts over each 6^(th) predefined time period orimpact frequency time period contained within the specific player'sphysiological parameter data. Specifically, this is done by addingtogether every impact matrix contained within one day to generate ahistorical summed daily impact matrix. An example of how matrixes can beadded together is shown in FIG. 21. Once all impact matrixes are addedtogether for one day, each entry in the historical summed daily impactmatrix is added together. For example, the number of historical impactsper day is equal to 13 for the example shown in FIG. 21. Then, theremote terminal 28 groups the number of historical impacts per day intodatasets based on the date they occurred, wherein the dataset boundariesare defined by the 6^(th) predefined amount of time. The 6^(th)predefined amount of time is set between 2 days and 90 days, preferablyset between 2 days and 30 days, and most preferably set to 7 days. Oncethe number of historical impacts per day has been grouped into thesedatasets, the remote terminal 28 averages the number of historicalimpacts per day that occurred within each of these datasets to determinethe average historical number of impacts over the 6^(th) time period.

Next, in step 602, the remote terminal 28 determines if the specificplayer's physiological parameter data contains enough impact data toperform the calculations involved with this training opportunity. Thishelps ensure that this training opportunity is not unnecessarilysuggested when there is not enough data for this training opportunity tobe accurately presented to the authorized user. The 11^(th) threshold orhistorical impacts threshold may be set to require at least 2 datasets,preferably at least 5 datasets, and most preferably at least 10datasets. If the specific player has not played long enough to recorddata over the required number of historical impacts threshold, then theremote terminal 28 performs no additional steps. However, if thespecific player has recorded data over the required number of historicalimpacts threshold, then, in step 604, the remote terminal 28 determinesthe baseline average number of impacts for the specific player.

Next, in step 606, the remote terminal 28 compares the baseline averagenumber of impacts to a 12^(th) threshold or a baseline number of impactsthreshold. The 8^(th) threshold may be set above 0.1 average number ofimpacts, preferably above 8 average number of impacts, and mostpreferably above 15 average number of impacts. If the specific player'sbaseline average number of impacts is not over the baseline number ofimpacts threshold, then the remote terminal 28 performs no additionalsteps. However, if the specific player's baseline average number ofimpacts is over the baseline number of impacts threshold, then, in step608, the remote terminal 28 determines the recent average number ofimpacts. Specifically, this is done by adding together every impactmatrix contained within one day to generate a recent summed daily impactmatrix. An example of how matrixes can be added together is shown inFIG. 21. Once all impact matrixes are added together for one day, eachentry in the recent summed daily impact matrix is added together. Forexample, this number of recent impacts per day is equal to 13 for theexample shown in FIG. 21. Then, the remote terminal 28 groups the numberof recent impacts per day into datasets based on the date they occurred,wherein the dataset boundaries are defined by the 6^(th) predefinedamount of time. Once the number of recent impacts per day has beengrouped into these datasets, the remote terminal 28 averages the numberof recent impacts per day that occurred within each of these datasets todetermine the recent average number of impacts over the 6^(th) timeperiod.

In step 610, the remote terminal 28 then compares the recent averagenumber of impacts against the 13^(th) threshold or the percent changeover the baseline average number of impacts threshold. The 13^(th)threshold may be set to 95 percentile of historical change based on aspecific player's position and playing level. If the recent averagenumber of impacts is not over the 13^(th) threshold, then the remoteterminal 28 performs no additional steps. However, if the recent averagenumber of impacts is over the 13^(th) threshold, then the trainingopportunity is triggered in step 612. This 5^(th) training opportunityinforms an authorized user that the specific player is experiencing moreimpacts than the specific player has previously experienced based on anaverage of their impact history. Accordingly, the authorized user shouldreview the specific player's form to see what has recently changed withthe specific player. For example, did the specific player recently comeback from an injury and is now favoring that side, which is causing thespecific player to have additional impacts.

Training opportunity algorithm 510 may be used to generate anothertraining opportunity by altering the data the algorithm 510 compares.Instead of comparing a specific player's recent data to the specificplayer's historical data, the training opportunity algorithm 510 maycompare a team's recent data to the team's historical data. The 15^(th)training opportunity or the increased number of impacts for team v.team's history training opportunity may be generally determined bycomparing a value that is derived from a team's recent recorded impactvalues with a value that is derived from the team's historical impactvalues. Specifically, values and data used in this 15^(th) trainingopportunity are described below:

-   -   a. Training Opportunity #5 or Increased Number of Impacts for        Specific Team v. Team's History        -   i. Comparison: specific team's recent number of impacts vs.            specific the team's historical number of impacts;        -   ii. Data Requirement: (i) number of recent impacts over a            6^(th) predefined amount of time for the specific team            and (ii) historical number of impacts over the 6^(th)            predefined amount of time for the specific team;        -   iii. 6^(th) Predefined Amount of Time or Impact Frequency            Time Period: set between 2 days and 90 days, preferably set            between 2 days and 30 days, and most preferably set to 7            days.        -   iv. 11^(th) Threshold or Historical Impact Threshold: set to            require at least 2 datasets, preferably at least 5 datasets,            and most preferably at least 10 datasets;        -   v. 12^(th) Threshold or Baseline Impact Threshold: set above            0.1 average number of impacts, preferably above 8 average            number of impacts, and most preferably above 15 average            number of impacts; and        -   vi. 13^(th) Threshold or Over Baseline Average Number Of            Impacts Threshold: set to 95^(th) percentile of change of            teams of similar skill level.

This 15^(th) training opportunity uses the same data flow that isdescribed above in connection with the 5^(th) training opportunityexcept for the fact that it utilizes different data sets. This 15^(th)training opportunity informs an authorized user that the team isexperiencing an increase in impacts in comparison to the team's history.Accordingly, a wholesome review of the specific teams playing styleshould be review.

Training opportunity algorithm 514 is shown in FIG. 17 and generallyrelates to a high impact load. The 6^(th) training opportunity or thehigh impact load for specific player v. nation training opportunity maybe generally determined by comparing a value that is derived from aspecific player's physiological parameter data with a value that isderived from the national physiological parameter data. In other words,the 6^(th) training opportunity may be generally determined by comparinga specific player value derived from data contained within the teamdatabase 32 with a value derived from data contained within the nationaldatabase 38 using algorithm 514. Specifically, values and data used inthis 6^(th) training opportunity include:

-   -   a. Training Opportunity #6 or High Impact Load for Specific        Player v. National Player        -   i. Comparison: specific player's impact load vs. historical            impact load experienced by similarly situated players in            terms of playing level and/or position;        -   ii. Data Requirement: (i) impact load over a 7^(th)            predefined amount of time for the specific player and (ii)            impact load over the 7^(th) predefined amount of time for            similarly situated players in terms of playing level and/or            position;        -   iii. 7^(th) Predefined Amount of Time or Impact Load Time            Period: set to any amount of time, preferably set between 2            days and 90 days, and most preferably set to 7 days; and        -   iv. 14^(th) Threshold or Impact Load Threshold: set to the            95^(th) percentile of the impact load that has historically            occurred over the 7^(th) predefined amount of time for            similarly situated players in terms of playing level and/or            position.

Specifically, the remote terminal 28 performs the steps described inalgorithm 514. First, in step 614, the remote terminal 28 sums up thetotal load the specific player experienced over a 7^(th) predefined timeperiod or an impact load time period. The 7^(th) predefined time periodmay be set to any amount of time, preferably set between 2 days and 90days, and most preferably set to 7 days. Specifically, this is done byadding together every impact matrix contained within the 7^(th)predefined time period to generate a summed impact load matrix. Anexample of how matrixes can be added together is shown in FIG. 21. Onceall impact matrixes are added together over the 7^(th) predefined timeperiod, each entry in the summed impact load matrix is weighted by aseverity value. This severity value is based on the severity of theimpact grouping contained within the impact matrix (e.g., 1^(st),2^(nd), 3^(rd), 4^(th) or 5^(th) severity), as described above inconnection with FIG. 12. Once each entry contained within the summedimpact load matrix is weighted by a severity value, the remote terminal28 added each of the entries together to determine the total impactload. For example, the total impact load for the bottom matrix shown inFIG. 21 would be equal to 35 (i.e., ((2 impacts)*(1 severityweight))+((4 impacts)*(2 severity weight))+((3 impacts)*(3 severityweight))+((4 impact)*(4 severity weight))). It should be understood thatdifferent severity weights may be used.

Once the remote terminal 28 has determined the total impact loadexperienced by the specific player over the impact load time period instep 614, this total impact load is compared with a 14^(th) threshold orimpact load threshold in step 616. The impact load threshold may be setto the 95^(th) percentile of the load that has historically beenexperienced over the impact load time period for similarly situatedplayers in terms of playing level and/or position. If the total impactload experienced by the specific player over the impact load time periodis less than impact load threshold, then the remote terminal 28 performsno additional steps. However, if the total impact load experienced bythe specific player over the impact load time period is greater than theimpact load threshold, then the 6^(th) training opportunity is triggeredin step 618. This 6^(th) training opportunity informs an authorized userthat the specific player is experiencing a higher impact load than othersimilarly situated players in terms of playing level and/or position.

As described above, training opportunity algorithm 514 may be used togenerate another training opportunity by altering the data the algorithm514 compares. Instead of comparing a specific player's data to nationaldata, the training opportunity algorithm 514 may compare a specificplayer's data to team data. The 12^(th) training opportunity or the highimpact load for specific player v. team training opportunity may begenerally determined by comparing a value that is derived from aspecific player's physiological parameter data with a value that isderived from the team's physiological parameter data. In other words,the 12th training opportunity may be generally determined by comparing aspecific player value derived from data contained within the teamdatabase 32 with a team value derived from data contained within theteam database 32 using algorithm 514. Specifically, values and data usedin this 12^(th) training opportunity include:

a. Training Opportunity #12 or High Impact Load for Specific Player v.Team

-   -   i. Comparison: specific player's impact load vs. historical        impact load experienced by teammates that play similar        positions;    -   ii. Data Requirement: (i) impact load over a 7^(th) predefined        amount of time for the specific player and (ii) impact load over        the 7^(th) predefined amount of time for teammates that play        similar positions;    -   iii. 7^(th) Predefined Amount of Time or Impact Load Period: set        to any amount of time, preferably set between 2 days and 90        days, and most preferably set to 7 days; and    -   iv. 14^(th) Threshold or Impact Load Threshold: set to the        95^(th) percentile of the impact load that has historically        occurred over the 7^(th) predefined amount of time for teammates        that play similar positions.

This 14^(th) training opportunity uses the same data flow that isdescribed above in connection with the 6^(th) training opportunity,except for the fact that it utilizes different data sets. This 14^(th)training opportunity informs an authorized user that the specific playeris carrying a high impact load than other team players that play similarpositions.

Training opportunity algorithm 514 may also be used to generate anothertraining opportunity by altering the data the algorithm 514 compares.Instead of comparing a specific player's data to national data, thetraining opportunity algorithm 514 compares a team's data to nationaldata. The 20^(th) training opportunity or the high impact load for teamv. national training opportunity may be generally determined bycomparing a value that is derived from a team's physiological parameterdata with a value that is derived from national physiological parameterdata. In other words, the 20^(th) training opportunity may be generallydetermined by comparing a team value derived from data contained withinthe team database 32 with a value derived from data contained within thenational database 38 using algorithm 504. Specifically, values and dataused in this 20^(th) training opportunity include:

-   -   a. Training Opportunity #20 or High Impact Load for Specific        Team v. National Team        -   i. Comparison: specific team's impact load vs. historical            impact load for teams of similar playing level;        -   ii. Data Requirement: (i) impact load over a 7^(th)            predefined amount of time for the teams and (ii) impact load            over the 7^(th) predefined amount of time for all teams of            similar playing level;        -   iii. 7^(th) Predefined Amount of Time or Impact Load Time            Period: set to any amount of time, preferably set between 2            days and 90 days, and most preferably set to 7 days; and        -   iv. 14^(th) Threshold or Impact Load Threshold: set to the            95^(th) percentile of the impact load that has historically            occurred over the 7^(th) predefined amount of time for teams            of similar playing level.

This 21^(st) training opportunity uses the same data flow that isdescribed above in connection with the 6^(th) training opportunity,except for the fact that it utilizes different data sets. This 21^(st)training opportunity informs an authorized user that the team isexperiencing more impacts than other teams that play at a similar level.Accordingly, a wholesome review of the specific teams playing styleshould be reviewed.

Training opportunity algorithm 516 is shown in FIG. 17 and generallyrelates to an increase in the impact load. The 7^(th) trainingopportunity or the increased impact load for specific player v. specificplayer's history training opportunity may be generally determined bycomparing a value that is derived from a specific player's recentlyrecorded physiological parameter data with a value that is derived froma specific player's historical recorded physiological parameter data. Inother words, the 7^(th) training opportunity may be generally determinedby comparing a specific player value derived from data contained withinthe team database 32 with a team value derived from data containedwithin the team database 32 using algorithm 516. Specifically, valuesand data used in this 7^(th) training opportunity include:

-   -   a. Training Opportunity #7 or Increased Impact Load for Specific        Player v. Specific Player's History        -   i. Comparison: specific player's recent impact load vs.            specific player's historical impact load;        -   ii. Data Requirement: (i) recent impact load over 8^(th)            predefined amount of time for the specific player and (ii)            historical impact load over the 8 ^(th) predefined amount of            time for the specific player;        -   iii. 8^(th) Predefined Amount of Time or Load Frequency Time            Period: set between 2 days and 90 days, preferably set            between 2 days and 30 days, and most preferably set to 7            days.        -   iv. 15^(th) Threshold or Historical Load Threshold: set to            require at least 2 datasets, preferably at least 5 datasets,            and most preferably at least 10 datasets;        -   v. 16^(th) Threshold or Baseline Load Threshold: set above            0.1 average number of impacts, preferably above 8 average            number of impacts, and most preferably above 15 average            number of impacts; and        -   vi. 17^(th) Threshold or Over Baseline Average Load            Threshold: set to 95th percentile of change of players of            similar skill level.

Specifically, the remote terminal 28 performs the steps described inalgorithm 516. Also, FIG. 24 provides an example of how the specificplayer's physiological parameter data may be analyzed by algorithm 516.First, in step 620, the remote terminal 28 determines the averagehistorical load over each 8^(th) predefined time period or loadfrequency time period contained within the specific player'sphysiological parameter data. Specifically, this is done by addingtogether every impact matrix contained within one day to generate ahistorical summed daily load matrix. An example of how matrixes can beadded together is shown in FIG. 21. Once all impact matrixes are addedtogether for one day, each entry in the historical summed daily impactload matrix is weighted by a severity value. This severity value isbased on the severity of the impact grouping contained within the impactmatrix (e.g., 1^(st), 2^(nd), 3^(rd), 4^(th) or 5^(th) severity), asdescribed above in connection with FIG. 12. Once each entry containedwithin the historical summed daily impact load matrix is weighted by aseverity value, the remote terminal 28 adds each of the entries togetherto generate a historical daily load. For example, the daily load for thebottom matrix shown in FIG. 21 would be equal to 35 (i.e., ((2impacts)*(1 severity weight))+((4 impacts)*(2 severity weight))+((3impacts)*(3 severity weight))+((4 impact)*(4 severity weight))). Then,the remote terminal 28 groups the historical daily loads into datasetsbased on the date they occurred, wherein the dataset boundaries aredefined by the 8^(th) predefined amount of time. The 8^(th) predefinedamount of time is set between 2 days and 90 days, preferably set between2 days and 30 days, and most preferably set to 7 days. Once thehistorical daily loads have been grouped into these datasets, the remoteterminal 28 averages the historical daily loads that occurred withineach of these datasets to determine the average historical impact loadover the 8^(th) time period.

Next, in step 622, the remote terminal 28 determines if the specificplayer's physiological parameter data contains enough data to performthe calculations involved with this training opportunity. This helpsensure that this training opportunity is not unnecessarily suggestedwhen there is not enough data for this training opportunity to beaccurately presented to the authorized user. The 15^(th) threshold orhistorical impact load threshold may be set to require at least 2datasets, preferably at least 5 datasets, and most preferably at least10 datasets. If the specific player has not played long enough to recorddata over the required historical impact load threshold, then the remoteterminal 28 performs no additional steps. However, if the specificplayer has recorded data over the required historical impact loadthreshold, then, in step 624, the remote terminal 28 determines thebaseline average impact load for the specific player.

Next, in step 626, the remote terminal 28 compares the baseline averageimpact load to a 16^(th) threshold or a baseline impact load threshold.The 16^(th) threshold may be set above 0.1 average impact load,preferably above 8 average impact load, and most preferably above 15average impact load. If the specific player's baseline average impactload is not over the baseline impact load threshold, then the remoteterminal 28 performs no additional sets. However, if the specificplayer's baseline average impact load is over the baseline impact loadthreshold, then, in step 628, the remote terminal 28 determines therecent average impact load. Specifically, this is done by addingtogether every impact matrix contained within one day to generate arecent summed daily load matrix. An example of how matrixes can be addedtogether is shown in FIG. 21. Once all recent impact matrixes are addedtogether for one day, each entry in the recent summed daily impact loadmatrix is weighted by a severity value. Once each entry contained withinthe recent summed daily impact load matrix is weighted by a severityvalue, the remote terminal 28 adds each of the entries together togenerate a recent daily load. For example, the daily load for the bottommatrix shown in FIG. 21 would be equal to 35 (i.e., ((2 impacts)*(1severity weight))+((4 impacts)*(2 severity weight))+((3 impacts)*(3severity weight))+((4 impact)*(4 severity weight))). Then, the remoteterminal 28 groups the recent daily loads into datasets based on thedate they occurred, wherein the dataset boundaries are defined by the8^(t)h predefined amount of time. Once the recent daily loads have beengrouped into these datasets, the remote terminal 28 averages the recentdaily loads that occurred within each of these datasets to determine theaverage recent impact load over the 8^(th) time period.

In step 630, the remote terminal 28 then compares the recent averageimpact load against the 17^(th) threshold or the percent change over thebaseline average impact load threshold in step 630. The 17^(th)threshold may be set to the 95^(th) percentile of historical changebased on a specific player's position and playing level. If the recentaverage impact load is not over the 17^(th) threshold, then the remoteterminal 28 performs no additional steps. However, if the recent averageimpact load is over the 17^(th) threshold, then the training opportunityis triggered in step 632. This 7^(th) training opportunity informs anauthorized user that the specific player has a higher impact load thanthe specific player has previously experienced based on an average oftheir impact history. Accordingly, the authorized user should review thespecific player's form to see what has recently changed with thespecific player. For example, did the specific player recently come backfrom an injury and is now favoring that side, which is causing thespecific player to carry additional impact load.

Training opportunity algorithm 516 may be used to generate anothertraining opportunity by altering the data the algorithm 516 compares.Instead of comparing a specific player's recent data to the specificplayer's historical data, the training opportunity algorithm 516 maycompare a team's recent data to the team's historical data. The 16^(th)training opportunity or the increased impact load for team v. team'shistory training opportunity may be generally determined by comparing avalue that is derived from a team's recent recorded impact values with avalue that is derived from the team's historical impact values.Specifically, values and data used in this 15^(th) training opportunityare described below:

-   -   a. Training Opportunity #16 or Increased Impact Load for        Specific Team v. Team's History        -   i. Comparison: team's recent impact load vs. team's            historical impact load;        -   ii. Data Requirement: (i) recent impact load over an 8^(th)            predefined amount of time for the team and (ii) historical            impact load over the 8^(th) predefined amount of time for            the team;        -   iii. 8^(th) Predefined Amount of Time or Load Frequency Time            Period: set between 2 days and 90 days, preferably set            between 2 days and 30 days, and most preferably set to 7            days.        -   iv. 15^(th) Threshold or Historical Load Threshold: set to            require at least 2 datasets, preferably at least 5 datasets,            and most preferably at least 10 datasets;        -   v. 16^(th) Threshold or Baseline Load Threshold: set above            0.1 average number of impacts, preferably above 8 average            number of impacts, and most preferably above 15 average            number of impacts; and        -   vi. 17^(th) Threshold or Over Baseline Average Load            Threshold: set to 95th percentile of change of teams of            similar skill level.

This 16^(th) training opportunity uses the same data flow that isdescribed above in connection with the 7^(th) training opportunityexcept for the fact that it utilizes different data sets. This 16^(th)training opportunity informs an authorized user that the team isexperiencing an increased impact load in comparison to the team'shistory. Accordingly, a wholesome review of the teams playing styleshould be review.

Training opportunity algorithm 518 is shown in FIG. 18 and generallyrelates to uncommon impact locations. The 8^(th) training opportunity orthe uncommon impact locations for specific player v. nation trainingopportunity may be generally determined by comparing a value that isderived from a specific player's physiological parameter data with avalue that is derived from the national physiological parameter data. Inother words, the 8^(th) training opportunity may be generally determinedby comparing a specific player value derived from data contained withinthe team database 32 with a value derived from data contained within thenational database 38 using algorithm 518. Specifically, values and dataused in this 8^(th) training opportunity include:

-   -   a. Training Opportunity #8 or Uncommon Impact Location for        Specific Player v. National Player        -   i. Comparison: location of impacts experienced by a specific            player vs. historical location of impact experienced by            similarly situated players in terms of playing level and/or            position;        -   ii. Data Requirement: (i) impact matrix determined over a            9^(th) predefined amount of time for the specific player            and (ii) impact matrix determined over the 9^(th) predefined            amount of time for similarly situated players in terms of            playing level and/or position;        -   iii. 9^(th) Predefined Amount of Time or Location Time            Period: set to any amount of time, preferably set between 2            days and 90 days, and most preferably set to 7 days;        -   iv. 18^(th) Threshold or Minimum Number of Impacts for            Location Analysis: set to require at least 1 impact,            preferably at least 10 impacts, and most preferably at least            15 impacts; and        -   v. 19^(th) Threshold or Location Threshold: set to the            95^(th) percentile of the chi-squared value based on impact            matrixes that have historically occurred over the 9^(th)            predefined amount of time for similarly situated players in            terms of playing level and/or position.

Specifically, the remote terminal 28 performs the steps described inalgorithm 518. First, in step 634, the remote terminal 28 determines ifthe specific player's physiological parameter data contains enoughimpact data to perform the calculations involved with this trainingopportunity. This helps ensure that this training opportunity is notunnecessarily suggested when there is not enough data for this trainingopportunity to be accurately presented to the authorized user. The18^(th) threshold or a minimum number of impacts for location analysisthreshold may be set to require at least 1 impact, preferably at least10 impacts, and most preferably at least 15 impacts. If the specificplayer has not played long enough to record data over the requiredminimum number of impacts for location analysis threshold, then theremote terminal 28 performs no additional steps. However, if thespecific player has recorded data over the required minimum number ofimpacts for location analysis threshold, then, in step 636, the remoteterminal 28 determines the summed location impact matrix over the 9^(th)predefined amount of time or location time period. The 9^(th) predefinedamount of time may be set between 2 days and 90 days, and mostpreferably set to 7 days. Specifically, this is done by adding togetherevery impact matrix contained within the 9^(th) predefined time periodto generate a summed location impact matrix. An example of how matrixescan be added together is shown in FIG. 21. Once all impact matrixes areadded together over the 9^(th) predefined time period, the entries foreach impact location (e.g., front, back, left, right, top) are summedtogether in step 636. For example, in FIG. 25A,P_(F)=P_(F1)+P_(F2)+P_(F3)+P_(F4)+P_(F5).

Once the remote terminal 28 has determined the summed location impactmatrix experienced by the specific player over the impact load timeperiod in step 636, this number is compared with a 19^(th) threshold orlocation threshold in step 638. The location threshold may be set to the95^(th) percentile of the chi-squared value based on impact matrixesthat have historically occurred over the 9^(th) predefined amount oftime for similarly situated players in terms of playing level and/orposition. Specifically, the table is shown in FIG. 25B shows anexemplary summed location impact matrix for the nation. This exemplarysummed location impact matrix for the nation is then compared againstthe summed location impact matrix experienced by the specific playerusing the formula shown in FIG. 25C to calculate an estimatedChi-Squared. The estimated Chi-Squared determined from the equationshown in FIG. 25C is compared against the value from the Chi-Squaredtable shown in FIG. 25E. In particular, for this equation the degrees offreedom are set to 1-number of elements (i.e., 4) and the conveniencelevel is set to the 95^(th) percentile. Tracing that these values acrossthe rows and columns in FIG. 25E, the Chi-Squared value is 0.711. Thus,if the estimated Chi-Squared value calculated using the equation in FIG.25C is greater than 0.711, then the 8^(th) training opportunity istriggered in step 640. However, if the estimated Chi-Squared valuecalculated using the equation in FIG. 25C is less than 0.711, thenremote terminal 28 performs no additional steps.

As described above, training opportunity algorithm 518 may be used togenerate another training opportunity by altering the data the algorithm518 compares. Instead of comparing a specific player's data to nationaldata, the training opportunity algorithm 518 may compare a specificplayer's data to team data. The 13^(th) training opportunity or theuncommon impact location for specific player v. team trainingopportunity may be generally determined by comparing a value that isderived from a specific player's physiological parameter data with avalue that is derived from the team's physiological parameter data. Inother words, the 13^(th) training opportunity may be generallydetermined by comparing a specific player value derived from datacontained within the team database 32 with a team value derived fromdata contained within the team database 32 using algorithm 518.Specifically, values and data used in this 13^(th) training opportunityinclude:

-   -   a. Training Opportunity #13 or Uncommon Impact Location for        Specific Player v. Team        -   i. Comparison: location of impacts experienced by a specific            player vs. historical location of impact experienced by            teammates that play similar positions;        -   ii. Data Requirement: (i) impact matrix determined over a            9^(th) predefined amount of time for the specific player            and (ii) impact matrix determined over the 9 ^(th)            predefined amount of time for teammates that play similar            positions;        -   iii. 9^(th) Predefined Amount of Time or Location Time            Period: set to any amount of time, preferably set between 2            days and 90 days, and most preferably set to 7 days;        -   iv. 18^(th) Threshold or Minimum Number of Impacts for            Location Analysis: set to require at least 1 impact,            preferably at least 10 impacts, and most preferably at least            15 impacts; and        -   v. 19^(th) Threshold or Location Threshold: set to the            95^(th) percentile of the chi-squared value based on impact            matrixes that have historically occurred over the 9^(th)            predefined amount of time for teammates that play similar            positions.

This 13^(th) training opportunity uses the same data flow that isdescribed above in connection with the 8^(th) training opportunity,except for the fact that it utilizes different data sets. This 13^(th)training opportunity informs an authorized user that the specific playerhas experienced impacts in uncommon locations when compared to otherteam players that play similar positions.

Training opportunity algorithm 518 may be used to generate anothertraining opportunity by altering the data the algorithm 518 compares.Instead of comparing a specific player's data to national data, thetraining opportunity algorithm 518 compares a team's data to nationaldata. The 21^(st) training opportunity or the uncommon impact locationfor team v. national training opportunity may be generally determined bycomparing a value that is derived from a team's physiological parameterdata with a value that is derived from national physiological parameterdata. In other words, the 21^(st) training opportunity may be generallydetermined by comparing a team value derived from data contained withinthe team database 32 with a value derived from data contained within thenational database 38 using algorithm 518. Specifically, values and dataused in this 21^(st) training opportunity include:

-   -   a. Training Opportunity #21 or Uncommon Impact Location for        Specific Team v. National Team        -   i. Comparison: location of impacts experienced by a team vs.            historical location of impact experienced by all teams of            similar playing level;        -   ii. Data Requirement: (i) impact matrix determined over a            9^(th) predefined amount of time for the teams and (ii)            impact matrix determined over the 9^(th) predefined amount            of time for all teams of similar playing;        -   iii. 9^(th) Predefined Amount of Time or Location Time            Period: set to any amount of time, preferably set between 2            days and 90 days, and most preferably set to 7 days;        -   iv. 18^(th) Threshold or Minimum Number of Impacts for            Location Analysis: set to require at least 1 impact,            preferably at least 10 impacts, and most preferably at least            15 impacts; and        -   v. 19^(th) Threshold or Location Threshold: set to the            95^(th) percentile of the chi-squared value based on impact            matrixes that have historically occurred over the 9^(th)            predefined amount of time for teams of similar playing            level.

This 21^(st) training opportunity uses the same data flow that isdescribed above in connection with the 8^(th) training opportunity,except for the fact that it utilizes different data sets. This 21^(st)training opportunity informs an authorized user that the specific teamis experiencing impacts in uncommon locations when compared to otherteams that play at a similar level. Accordingly, a wholesome review ofthe specific teams playing style should be reviewed.

It should be understood that the system 10 may include only some of theabove described algorithms or it may contain additional algorithms thatwere no discussed above. For example, the system 10 may include trainingopportunities that are based on one or more tools/approaches: (i) BayesTheorem, (ii) standard t-tests or ANOVA, (iii) changes in kurtosis, (iv)Kruskal Wallis non-parametric distribution testing, (v) machine learningusing various neural network topologies including RNN and LSTM, (vi)pattern detection using Support Vector Machines (SVM), (vii) principalcomponent Analysis (PCA), (viii) Independent Component Analysis (ICA),(ix) clustering approaches including k-nearest neighbor or (x) othersimilar techniques.

3. Updating the Threshold Values

As described above, each training opportunity is determined by thesystem 10 based on comparing datasets to one another using variousalgorithms Each of these algorithms contains at least one thresholdvalue (e.g., 1^(st) threshold-19^(th) threshold) and some of thesealgorithms contain predefined amounts of time (e.g., 1^(st) predefinedamount of time-9^(th) predefined amount of time). These threshold valuesand the predefined amounts of time can be updated by the systemadministrator. Specifically, the system administrator could update thesevalues by pushing an update to the individual remote terminals 28 of thesystem 10. Alternatively, if all of the calculations are remotelyperformed within a cloud server, then the system administrator canupdate the values within that server.

Alternatively, the system 10 may utilize self-updating threshold valuesin connection with certain algorithms These self-updating thresholdvalues are different than the threshold values that can be manuallyupdated (e.g., the threshold values and predefined amounts of time)because they do not require human (e g , system administrator)intervention. These self-updating thresholds provide a significantadvantage over to the system 10 over conventional systems because itallows the system 10 to adapt to how the activity is currently beingplayed. For example, changes to the rules of the activity and/orimprovements in the protective equipment worn by specific players maysignificantly affect what a specific player experiences (e.g., impacts)during the activity. These significant alterations in what the specificplayer experiences (e.g., impacts) may alter the physiological parameterdata (e.g., magnitude of impacts) that is recorded from theseactivities. Specifically, football helmets that were recently developedare testing over 20% better on the NFL's Helmet Laboratory Testing incomparison to football helmets that were developed fifteen yearsearlier. Without using self-updating threshold values, some of the abovedescribed training opportunities may not be accurately monitored andtriggered and thus are not useful for the authorized user. Additionally,self-updating thresholds allow the system 10 to selectively tailor theamount of data that is the system 10 monitors and process in connectionwith each algorithm. Specifically, tailoring of the amount of data thatis processed is accomplished by selecting narrower threshold values whennecessary or broader threshold values when necessary. This selectivebalancing reduces processing requirements, increases efficiencies,decreases battery/power consumption, and provides other likely benefitsto the system 10.

In certain embodiments, the following threshold values can beself-updating: (i) 2^(nd) threshold or high magnitude impact threshold,(ii) 3^(rd) threshold or single impact alert threshold, (iii) 4^(th)threshold or a cumulative impact alert threshold, (iv) 5^(th) thresholdor number of alertable impacts threshold, (v) 6^(th) threshold or numberof high magnitude impacts threshold, (vi) 10^(th) threshold or number ofimpacts threshold, (vii) 13^(th) threshold or over baseline averagenumber of impacts threshold, (viii) 14^(th) threshold or impact loadthreshold, (ix) 17^(th) threshold or over baseline average loadthreshold and (x) 19^(th) threshold or location threshold. Specifically,FIG. 19 shows one example of how these self-updating threshold valuescan be updated or recalculated using algorithm 700. The system 10reviews when the current self-updating threshold value was last updatedin step 702. If a 10^(th) predetermined amount of time has passed sincethis value was last updated, then the system 10 will recalculate theself-updating threshold values based on all data contained within thedatabases (e.g., team database 32 and national database 38), which isassociated with the self-updating threshold value that is beingrecalculated in step 704. For example, the data set that is used tocalculate the self-updating threshold value may contain 10,000 impactsbefore the expiration of the 10^(th) predetermined amount of time andmay have 20,000 impacts after the expiration of the 10^(th)predetermined amount of time. It should be understood that the 10^(th)predefined time period may be greater than 1 day and preferably greaterthan 1 week. Additionally, the 10^(th) predefined time period may be setto different lengths of time for each self-updating threshold value. Ifthe 10^(th) predetermined amount of time has not passed since this valuewas last updated or the recalculated self-updating threshold values arenot different than the current self-updating threshold values, then thesystem 10 will perform no additional steps and the current self-updatingthreshold values will be kept. However, if one of the self-updatingthreshold value is different in step 706, then the system 10automatically replaces the current self-updating threshold value withthe recalculated self-updating threshold value in step 708. Theserecalculated self-updating threshold values are then downloaded by theremote terminals 28. It should be understood that all threshold valuesmay not be updated at the same time. Alternatively, if all of thecalculations are remotely performed within a cloud server, then thesystem 10 can update the self-updating threshold values within thatserver.

In addition to the above described steps, an alternative embodiment mayperform an additional step to ensure that the self-updating thresholdvalues are not being replaced too frequently. To help ensure this, theself-updating threshold values are only replaced if they aresignificantly different than the current self-updating threshold value.Significantly different in this context may mean where the recalculatedself-updating threshold value is greater than 5% different than thecurrent self-updating threshold value. Alternatively, significantlydifferent could mean where the recalculated self-updating thresholdvalue is greater than 15% different than the current self-updatingthreshold value.

Instead of calculating the self-updating threshold values from all datacontained within the databases (e.g., team database 32 and nationaldatabase 38) that is associated with the self-updating threshold value,the self-updating threshold values may be calculated based on a subsetof data contained within the databases (e.g., team database 32 andnational database 38) that is associated with the self-updatingthreshold value. Specifically, in this alternative exemplary embodiment,the self-updating threshold values may be calculated based on aweighting of all relevant data. To determine this weighted self-updatingthreshold values, all relevant data is weighted by a decaying factor.For example, data recorded 5 years ago may be multiplied by 0.5 decayingfactor, thereby reducing the value of this data. It should be understoodthat certain data will be excluded from this calculation because is oldenough to cause its weighting value to be zero due to the decayingfactor. For example, if the decaying factor for data that is over 10years old is 0; then regardless of the value of the data, this data isirrelevant to this calculation and will not be included within thiscalculation. One skilled in the art recognizes that weighting variables(e.g., time window and decay function) are adjustable. Calculating theself-updating threshold values in the manner described within thisalternative embodiment may be beneficial because it excludes data thatmay be skewing the self-updating threshold values. For example, datathat was recorded prior to significant rule changes and/or significantimprovements in protective equipment.

Instead of calculating the self-updating threshold values using aweighted average, the system 10 may calculate the self-updatingthreshold values by simply excluding relevant data from the calculationthat occurred before a predetermined amount of time. For example,relevant data that was collected over 15 years ago may be excluded. Asdescribed above, calculating the self-updating threshold values in thismanner may be beneficial because it excludes data that may be skewingthe self-updating threshold values. In a further embodiment, theself-updating threshold values may be calculated using a combination ofthe above techniques or methods.

i. GUI

Below is a high level description of the user interface that may beshown on the display 28 a of the remote terminal 28 to inform the userof the training opportunities, other information based on the recordedphysiological parameter data and/or other information that has beendetermined by the system 10 or information that the system 10 obtainedfrom another source. The remote terminal 28 may also peripheral devices28 b that allow the authorized user to interact with the GUI. FIG. 26illustrates a first screen of a GUI 1000 of the system 10 that may beshown to the authorized user on the display 28 a of the remote terminal28 or a device that is combined with the remote terminal 28 (e.g. deviceshown in FIGS. 3 and 6). Specifically, the screen is shown in FIG. 26 isa dashboard 1002 that is the landing page that is displayed after theauthorized user logs into the system 10. The dashboard 1002 includes arow of buttons 1004 on the top of the screen, wherein these buttonsinclude a dashboard button 1006, coaching tool button 1008, impactanalysis button 1010, reports button 1012, team setup button 1014, andan administrator button 1016. The authorized user can utilize theperipheral devices 28 b to navigate to other sections/tool containedwithin the GUI 1000 using buttons 1004. To indicate which screen theuser is currently viewing, one of the buttons 1006-1016 in the row ofbuttons 1004 is identified (e.g., illuminated in a different color). Forexample, FIG. 26 shows that the dashboard button 1006 in a differentcolor than the rest of the buttons 1008-1016 contained within the row ofbuttons 1004. Setting aside the row of buttons 1004 that is displayed onevery screen of the GUI 1000, the dashboard 1002 page specificallyincludes user notifications 1020 (e.g., new alerts 1024 and new trainingopportunities 1028), practice planner 1040, a quick list that includes alist of players 1060, a player report 1080, and position-based insights1090.

The quick list 1060 is designed to show a subset of the information(e.g., identifier 1062, name 1064, position 1066, single impact alerts1068, multiple impacts alerts 1070, and training opportunities 1072)about a subset of the players to allow the authorized user keep abreastof these players without going through multiple screens contained withinthe GUI 1000. The authorized user can add players to the quick list 1060using the select player feature 1074 and can remove players from thequick list 1060 by pressing the “X” 1076 that is associated with theplayer. The player report 1080 section allows the authorized user togenerate a report about a specific player. Additional details aboutthese reports will be discussed in connection with FIGS. 54-56. Finally,the authorized user can use position based insights 1090 section torequest impact analytics in connection with a unit or position. Forexample, the selection of the button 1092 will navigate to a screen thatdisplays the HIE load statistics for the past week in connection withthe defense unit; an example of this screen is shown in FIG. 41.Additionally, the selection of the button 1094 will navigate to a screenthat displays the HIE load statistics for the past week in connectionwith the linebacker position; an example of this screen is shown in FIG.44.

It should be understood that this dashboard 1002 provides a significantimprovement in the efficiency of using the system 10 by bringingtogether and effectively visually presenting a limited list of highpriority information without requiring the user to navigate throughmultiple screens in order to obtain this information. This in turnimproves the efficiency of using the system 10 because it saves the userform navigating to a selected screen, manipulating the data associatedwith that screen, and then trying to interpret the resulting data. Thesefactors tangibly improve the functionality of the system 10,particularly the user interface, and more particularly effectivelydisplaying the user interface on a remote terminal 28 that has a smallscreen (e.g., mobile phone).

An authorized user can leave the dashboard 1002 and navigate to a firstscreen 1100 contained within the coaches tool 1110. FIG. 27 shows anexemplary graphical representation of the first screen 1100, which maybe displayed by the remote terminal 28 within the system 10.Specifically, this first screen 1100 may be displayed by selectingbutton 1008 and the month view button 1114. The coaches tool 1110includes a monthly view 1132 of a practice planner 1130, monthly view1148 of an impact trend chart 1150, monthly view 1168 of an alert chart1170, and monthly view 1188 of a training opportunities chart 1090(shown in FIGS. 27 and 28C, but not in FIG. 28D). As best shown in FIG.28A, the monthly view 1132 of the practice planner 1130 shows a highlevel view of the number of alerts 1134 and the number of trainingopportunities 1136 that were recorded on each day. Also, the monthlyview 1132 of the practice planner 1130 displays whether a game or apractice occurred on the specific days by using the letters “P” or “G”in the upper left area 1138 of each day. It should be understood thatmore or less information may be displayed within the monthly view 1132of the practice planner 1130.

The impact trend chart 1150 shown in FIGS. 27 and 28B show the impacttrends for the entire team over the selected month (e.g., September2017). In particular, the impact trend chart 1150 includes an X-axis1152, which has days of the month, and a Y-axis 1154, which has impactcount. Bars are displayed within the impact chart, which represents thetotal number of impact recorded by the team over each of the days. Forexample, 9/30 or September 30^(th) the team recorded approximately 320total impacts 1156, where approximately 240 were of low magnitude 1158,approximately 70 were of medium magnitude 1160, and approximately 10were of high magnitude 1162. It should be understood that the high,medium and low categories of impacts 1158, 1160, 1162 are derived fromthe severity levels contained within the impact matrix. Specifically,severity levels 1 and 2 are considered low magnitude impacts shown ingray 1158, severity levels 3 and 4 are considered medium magnitudeimpacts showing in yellow 1160, and severity level 5 is considered ahigh magnitude impact is shown in orange 1162.

The alert chart 1170 shown in FIGS. 27 and 28C show a subset of thealert data that was collected based on algorithm 502 for the team overthe selected month (e.g., September 2017). In particular, the alertcharts 1170 shows: alert date 1072, player identifier (e.g., number)1174, player name 1176, player position 1178, alert time 1180, alerttype 1182, and alert location 1184. In this chart, the alert type 1182can be either a single impact or a cumulative impact alert.Specifically, the single impact alert is triggered in step 546 ofalgorithm 502 and the cumulative impact alert is triggered in step 552of algorithm 502. It should also be understood that this alert chart1170 only displays a portion of the alert data and that in otherembodiments the chart may include other alerts and/or more/less of thealert data. The alert time 1180 may be used by the authorized user tocorrelate this alert with the impact the player experienced, which canbe shown on the videotape of the game. This may aid the authorizeduser's ability to help the player avoid these alerts in the future. Inan alternative embodiment, the game tape may automatically be syncedwith the videotape from the game and selection of the alert type maypull up 1 minute of videotape prior to the impact and 1 minute ofvideotape after the impact. In a further alternative embodiment, asopposed to the text set forth under alert location 1184, the alert chart1170 may include a graphical representation of a specific player's headshowing the location of the impact.

The training opportunities chart 1190 shown in FIG. 28D shows a subsetof the training opportunities that were triggered based on the aboveeight algorithms 504, 506, 508, 510, 512, 514, 516, 518 for the teamover the selected month (e.g., September 2017). In particular, thetraining opportunities chart 1190 shows: training opportunity date 1191,specific player identifier (e.g., number) 1192, specific player name1193, specific player position 1194, and an icon or indicator thatrepresents the type of training opportunity 1195. Here, the first iconor indicator 1196 represents training opportunities that are based onintensity, which include training opportunities that are determinedusing algorithms 504, 506 and 508. Specifically, these trainingopportunities are shown in FIG. 28D are based upon trainingopportunities #1, #2, and/or #3, as these are training opportunities forthe individual players. The second icon 1197 represents trainingopportunities that are based on location, which include trainingopportunities are determined using algorithm 518. Specifically, thesetraining opportunities are shown in FIG. 28D are based upon trainingopportunity #8, as these are training opportunities for the individualplayers. The third icon 1197 represents training opportunities that arebased on volume, which include training opportunities that aredetermined using algorithms 510 and 512. Specifically, these trainingopportunities are shown in FIG. 28D are based upon trainingopportunities #4 and/or #5, as these are training opportunities for thespecific players. The fourth icon 1199 represents training opportunitiesthat are based on load, which include training opportunities that aredetermined using algorithms 514 and 516. Specifically, these trainingopportunities are shown in FIG. 28D are based upon trainingopportunities #6 and/or #7, as these are training opportunities for theindividual players. It should be understood that more or less trainingopportunities may be present within this training opportunities chart1190. For example, the training opportunities chart 1190 may alsocontain training opportunities based on local or regional data setsinstead of national data.

An authorized user can leave the first screen 1100 contained within thecoaches tool 1110 and navigate to a second screen 1200 contained withinthe coaches tool 1110. FIG. 29 shows an exemplary graphicalrepresentation of the second screen 1200, which may be displayed by theremote terminal 28 within the system 10. Specifically, this secondscreen 1200 may be displayed by: (i) selecting a single day containedwithin monthly view 1132 of the practice planner 1130 or (ii) selectingthe day view button 1115 from practice planner 1130 instead of selectingthe month view button 1114. The second screen 1200 contained within thecoaches tool 1110 includes a team daily view 1210 of the practiceplanner 1130, team daily view 1230 of the impact trend chart 1150, teamdaily view 1250 of the alert chart 1170, and team daily view 1270 of thetraining opportunities chart 1270 (shown in FIG. 34, but not in FIG. 29or 30C).

The practice planner 1150 shown in FIG. 29 can be edited by selectingthe edit session button 1214, which brings up the screen 1300 shown inFIG. 30A. From this screen 1300 shown in FIG. 30A, the authorized usercan edit the practice plan. Edits to the practice plan may includespecifying the dress type (e.g., walkthrough, helmets only,uppers/shells, full pads) 1308, start time 1310, session type (e.g.,practice, game, scrimmage) 1312. Additionally, the authorized user mayspecify how the practice will be broken down into individual components.For example, the authorized user may specify: (i) start time for theperiod 1314, (ii) duration of the period 1316, (iii) name of the period1318, (iv) drill (e.g., stretch & agility, cage drill: D, outsidestretch prep, etc.) 1320, (v) unit (e.g., entire team, subset of theteam) 1322, (vi) positions (e.g., lineman, running back, etc.) 1324, and(vii) contact level 1326. It should be understood that multiple drillsin 1320 may be added to a single time period 1318. To remove the timeperiod, the user selected the “X” 1330 on the right-hand side of thescreen. It should be understood that the practice plan and edits theretomay include additional, fewer, or other options tailored to differentsports.

Once the authorized user is finished editing the practice plan usingscreen 1300, the system 10 will calculate the total number of projectedfull contact minutes 1350. This number can be utilized by the authorizeduser to players who are not subject to too many full contact minutes perweek/per month. Additionally, the drills 1320 in combination with thetotal number of projected full contact minutes 1350 may be utilized bythe system to project how many impact alerts and training opportunitieswill occur during the practice. Specifically, the system 10 may utilizea learning algorithm that studies the team's alert data and impactmatrixes that are generated during various drills in 1320. Thisprojection of the number of impact alerts and training opportunities mayalso be used by the authorized user to help ensure that the players arenot placed in a position to experience many alerts/trainingopportunities per week/ month. It should be understood that theauthorized user may make the determination of how many full contactminutes and or projected alerts/training opportunities are acceptableper week by setting a 20^(th) threshold value or acceptable thresholdwithin the system 10. In this embodiment, the system 10 will compare theprojections against this acceptable threshold and will provide warningsto the user, if the user deigns a practice plan that exceeds thisprojected threshold. Alternatively, system 10 may determine the 20^(th)threshold value or acceptable threshold based on an analysis of the dataassociated with teams that play at a similar level. Like the aboveembodiment, the system 10 will provide the user with a warning to theuser, if the user deigns a practice plan that exceeds this projectedthreshold.

Once the authorized user is satisfied with the practice plan, theauthorized user can save the plan by selecting the save button 1305.Selecting the save button 1305 will send the user back to screen 1200.However, the updated version of the practice plan will replace thepreviously displayed version of the practice plan upon the user'sarrival at screen 1200. Once back at screen 1200, the user can email1202, print 1204 the practice plan, or provide additional notes 1206about the practice plan. It should be understood that the system 10 mayprovide other options (e.g., request comments from another user, publisha practice plan to players, etc.) to the user in connection with thepractice plan.

FIG. 30C shows a portion of the second screen 1200, wherein only theteam daily view 1230 of the impact chart 1150 and the team daily view1250 of the alert chart 1170 are shown. Specifically, this team dailyview 1230 of the impact chart 1150 shows the total number of impact thatoccurred during the day 1234, the total number of specific player's thatexperienced an impact during the day 1236, the time periods thatcorrelate to the practice plan 1238, number of impacts broken down overa time period that is based on the practice plan 1240, magnitude of theimpacts 1242 that the team recorded during the selected day (e.g., Sep.6, 2017). As described above in connection with FIG. 28B, the magnitudeof the impacts 1242 are grouped into high, medium and low categories1158, 1160, 1162, wherein severity levels 1 and 2 are considered lowmagnitude impacts shown in gray 1158, severity levels 3 and 4 areconsidered medium magnitude impacts showing in yellow 1160, and severitylevel 5 is considered a high magnitude impact is shown in orange 1162.It should be understood that the groupings of the time periods 1238 andthe magnitude of impacts 1242 may be grouped in other manners.

Also, this team daily view 1250 of the alert chart 1170 shows the playeridentifier (e.g., number) 1254, player name 1256, player position 1258,alert time 1260, alert type (e.g., single or cumulative) 1262 and alertlocation 1264 that the team recorded during the selected day (e.g.,September 16). Instead of viewing the alert chart 1170, the authorizeduser can view the training opportunities chart 1190 by selecting button1298, shown in FIG. 30C. The selection of button 1298 replaces the alertchart 1170, shown in FIG. 30C, with the training opportunities chart1190, shown in FIG. 34. In particular, this team daily view 1270 of thetraining opportunities chart 1190 shows the player identifier (e.g.,number) 1274, player name 1276, player position 1278, date 1280, andtraining opportunity type 1282 that the individual players within theteam recorded during the selected day (e.g., Sep. 6, 2017).

From the second screen 1200, the authorized user can select the unitbutton 1406 or select the arrow button 1410. The selection of either oneof these buttons 1406, 1410, replaces the team daily view 1230 of theimpact chart 1150 with a unit daily view 1430 of the impact chart 1150.In particular, the unit screen 1400 shows the impacts that the selectedunit (e.g., offense) 1412 recorded during the selected day (e.g., Sep.6, 2017). Specifically, this unit daily view 1430 of the impact chart1150 shows the total number of impact that occurred during the daywithin the selected unit 1434, the total number of specific player'sthat are within the selected unit and experienced an impact during theday 1436, the time periods that correlate to the practice plan 1438,number of impacts broken down over a time period that is based on thepractice plan 1440, magnitude of the impacts 1442 that the unit recordedduring the selected day (e.g., Sep. 6, 2017).

Also, in connection with FIG. 31, the left side of the unit daily view1430 of the impact chart 1150 displays the number of alerts over theselected day for the selected unit 1410, number of trainingopportunities over the selected day for the selected unit 1414, and thetotal number of impacts that occurred during the day 1434. In additionto the above information that is displayed within the chart 1150, theauthorized user can hover over an extent of a bar on chart 1150 toobtain additional data about that extent of the bar. For example, FIG.31 shows additional bar segment information 1460 by hovering over a barthat correlates to medium magnitude impacts that occurred from 3:45-3:50on Sep. 6, 2017. This bar segment information 1460 includes: (i) numberof impact 1464, (ii) percentage of impact that are contained within theselected segment in comparison to all impact contained within the bar1466, (iii) total number of impacts contained within the bar 1468, (iv)player who had the most impacts within the selected bar segment (e.g.,top contributor) 1470, and (v) the number of impacts that wasexperienced by top contributor 1472. It should be understood that thisbar segment information 1460 may be displayed in connection with anychart described herein.

From the second screen 1200, the authorized user can select the positionbutton 1508. Alternately, the authorized user may select the arrowbutton 1511 from the screen 1400 shown in FIG. 31. The selection ofeither one of these buttons 1508, 1511, replaces either: (i) the teamdaily view 1230 of the impact chart 1150 or (ii) unit daily view 1430 ofthe impact chart 1150 with a position daily view 1530 of the impactchart 1150. In particular, the position screen 1500 shows the impactsthat the selected position (e.g., running back) 1512 recorded during theselected day (e.g., Sep. 6, 2017). Specifically, this position dailyview 1530 of the impact chart 1150 shows the total number of impact thatoccurred during the day for the position 1534, the total number ofspecific player's that experienced an impact during the day that playthe selected position 1536, the time periods that correlate to thepractice plan 1538, number of impacts broken down over a time periodthat is based on the practice plan 1540, magnitude of the impacts 1542that the position recorded during the selected day (e.g., Sep. 6, 2017).Also, in connection with FIG. 32, the left side of the position dailyview 1530 of the impact chart 1150 displays the number of alerts overthe selected day for the selected position 1510, number of trainingopportunities over the selected day for the selected unit 1414, and thetotal number of impacts that occurred during the day 1434.

From the second screen 1200, the authorized user can select the positionbutton 1609. Alternately, the authorized user may select the arrowbutton 1613 from the screen 1600 shown in FIG. 33. The selection ofeither one of these buttons 1609, 1613, replaces either: (i) the teamdaily view 1230 of the impact chart 1150 or (ii) position daily view1530 of the impact chart 1150 with a player daily view 1630 of theimpact chart 1150. In particular, the player screen 1600 shows theimpacts that the selected player (e.g., Conradrb Collins) 1612 recordedduring the selected day (e.g., Sep. 6, 2017). Specifically, this playerdaily view 1630 of the impact chart 1150 shows the total number ofimpact that occurred during the day for the player 1534, the timeperiods that correlate to the practice plan 1638, number of impactsbroken down over a time period that is based on the practice plan 1640,magnitude of the impacts 1642 that the player recorded during theselected day (e.g., Sep. 6, 2017). Also, in connection with FIG. 32, theleft side of the player daily view 1630 of the impact chart 1150displays the number of alerts over the selected day for the selectedplayer 1610, number of training opportunities over the selected day forthe selected player 1614, and the total number of impacts that occurredduring the day 1634.

As shown in FIG. 34, the authorized user can view the trainingopportunities chart 1190 by selecting button 1298, shown in FIG. 33.After displaying the training opportunities chart 1190, the authorizeduser can gain additional information about each of the trainingopportunities by selecting the training opportunity icons 1196, 1197,1198, and 1199 on the right side of the screen. For example, if theauthorized user may select icon or indicator 1197 that is associatedwith Jesse Katz, the specific player training opportunity screen orplayer report 1700 is displayed for the selected training opportunity inFIG. 35. Referring to FIG. 35, the specific player training opportunityscreen or player report 1700 is derived from the physiological data thatwas collected using at least one of the eight algorithms (e.g., 504,506, 508, 510, 512, 514, 516, and 518) discussed above. The specificplayer report displays: (i) the report time period 1720, (ii) specificplayer's name 1722, (iii) player details 1724 (e.g., specific player'snumber and specific player's position), (iv) training opportunities 1726triggered by a player during the report time period, (v) the number ofalerts 1728 triggered by a player during the report time period, (vi)the number of impacts 1730 experienced by a player during the reporttime period, (vii) graphical representation of the impact locations 1732the experienced by a player during the report time period, wherein thepercentages of impacts are shown below the graphical representations ofthe headform, (viii) alert date 1736, (ix) alert type 1738, (x) alertlocation 1740, (xi) reporting view 1750 of the impact chart 1150, and(xii) training opportunity date 1742.

The specific player report 1700 for Jesse Katz, shown in FIG. 35 showsthat the player triggered two locations based on training opportunity1196 on Aug. 31, 2017 and Sept. 6, 2017. This training opportunity wasbased upon the specific player experiencing an uncommon impact patterncompared to national norms. This training opportunity was determinedusing algorithm 518 and is training opportunity #8. It should beunderstood that this specific player report 1700 provides a significantimprovement in the efficiency of using the system 10 by bringingtogether and effectively visually presenting a limited list of highpriority information without requiring the user to navigate throughmultiple screens in order to obtain this information. This in turnimproves the efficiency of using the system 10 because it saves the userform navigating to a selected screen, manipulating the data associatedwith that screen, and then trying to interpret the resulting data. Thesefactors tangibly improve the functionality of the system 10,particularly the user interface, and more particularly effectivelydisplaying the user interface on a remote terminal 28 that has a smallscreen (e.g., mobile phone).

FIGS. 36-49 show various screens 1800 that are associated with theimpact analytics tool 1010. Specifically, the authorized user willnavigate to the impact analysis screen 1800 by selecting button 1010.The impact analysis tool 1010 allows the authorized user to visually seea particular set of information and/or compare various data setscontained within the physiological parameter data. Specifically, theauthorized user can select to see data associated with the team, with aunit by selecting the unit from the drop-down 1710, with a position byselecting the position from the drop-down 1712, with a player byselecting the player from the drop-down 1714. After or before, theauthorized user can also select the desired time period 1716, theauthorized user can also select the data the user desires to view usingbutton 1718. In the exemplary embodiment disclosed herein, the viewabledata includes: HIE load 1720, HITsp 1722, and HIE location 1724. Inparticular, FIG. 36 show a view of an HIE load screen 1800 that includesreporting period view 1830 of the impact chart 1150 and reporting periodview 1850 of the alert chart 1170. Additionally, FIG. 37 shows a screen1900 that is a zoomed in version of screen 1800, where only thereporting period view 1830 of the impact chart 1150 is viewable. Thischart is broken down in the same manner as the charts discussed above inconnection with FIGS. 27, 28B, 28C, as such the above descriptions applyin equal force to this chart.

FIG. 38 shows a view of a HITsp screen 2000, which can be reached byselecting HITsp 1822 from the 1818 dropdown button on screen 1800 shownin FIG. 36. By making this selection, the reporting period view 1830 ofthe impact chart 1150 is replaced with a reporting period view 2020 ofthe HITsp chart 2040. The HITsp chart 2040 shows the single alerts 2050and cumulative alerts 2060 that were recorded by the HIU 22 over thereporting period. Additionally, as discussed above in connection withFIG. 31, the authorized user can hover their mouse over a section of thegraph to obtain additional information. In FIG. 38, additionalinformation is displayed about the single alerts that occurred on Nov.25, 2017. Specifically, there were four single alerts that occurred onNov. 25, 2017, which made up 14.8% of the total alerts (i.e., 27) thatoccurred on Nov. 25, 2017.

FIG. 39 shows a view of an HIE location screen 2100, which can bereached by selecting HIE location 1824 from the 1818 dropdown button onscreen 1800 shown in FIG. 36. By making this selection, the reportingperiod view 1830 of the impact chart 1150 is replaced with a reportingperiod view 2120 of the HIT location chart 2140. The HIT location chart2140 shows the impact locations that were recorded by the HIU 22 overthe reporting period and their associated severity level (e.g., low,medium, and high). In addition to including a breakdown of these HITlocations in chart 2140, the HIE location screen 2100 also includes agraphical representation 2150 of a player's head that shows the HIElocation that makes up a majority of the impact that is shown within thereporting period.

Similar to the transitional functionality between FIGS. 30C and 31, theauthorized user may select a desired unit from the drop-down usingbutton 1810 or may select the arrow 1890 to transition from the team HIEload screen 1800, as shown in FIG. 37, to a unit HIE load screen 2200,shown in FIG. 40. The only difference between the team HIE load screen1800 to the unit HIE load screen 2200 is the fact that the HIE load datais displayed for a different player grouping (e.g., defense instead ofthe team). Likewise, the authorized user may select a desired unit fromthe drop-down using button 1810 or may select the arrow 2090 totransition from the team HITsp screen 2000, as shown in FIG. 38, to aunit HITsp screen 2300, shown in FIG. 41. The only difference betweenthe team HITsp screen 2000 to the unit HITsp screen 2300 is the factthat the HITsp data is displayed for a different player grouping (e.g.,defense instead of the team). Further, the authorized user may select adesired unit from the drop-down using button 1810 or may select thearrow 2190 to transition from the team HIE location screen 2100, shownin FIG. 39, to a unit HIE location screen 2400, shown in FIG. 42. Theonly difference between the team HIE location screen 2100 to the unitHIE location screen 2400 is the fact that the HIE location data isdisplayed for a different player grouping (e.g., defense instead of theteam). As described above, different groupings of players (e.g., team,unit, position) may be selected for analysis. Another example of adifferent grouping is shown in connection with FIG. 43, which displaysposition HIE load screen 2500 for linebackers of the reporting period.It should be understood that other grouping of players may be selected,other recorded physiological parameter data may be displayed, otherreporting ranges may be picked to display information that will aid theauthorized user in training players and developing practice plans.

In contrast to the data shown in FIGS. 36-43 that display data relatedto a group of players, FIGS. 44-48 show various screen 2600, 2700 2800,2900, and 3000 that display data related to one specific player. Thisallows the authorized user to understand the HIE load, HITsp, and HIElocation of the impacts the specific player (e.g., Vin Malloy) hasexperienced over the reporting time period. In addition to displayingthe HIE load, HITsp, and HIE location for the player, the authorizeduser can also graphically compare this data against data that isassociated with other groups within the team. As shown in FIG. 44, theauthorized user can compare Vin Malloy's HIE load, shown by the barswithin the graph, for the reporting time period against the average HIEload for the team, shown by the black line 2610. This information allowsthe authorized user to make determinations about how the player iscomparing, on average, against other groups within the team. Forexample, is this player far more impacts than other players within theteam that is participating in the same activities. In addition tocomparing the team's data against the player's data, the system can alsocompare any one of the following: team, unit, position, player againstany one of the following: team, unit, position, player. It should beunderstood that other comparisons could be made.

Referring to FIG. 49, the authorized user can gain additionalinformation about each of the training opportunities by selecting thetraining opportunity icons 1196, 1197, 1198, and 1199 on the right sideof the screen. For example, if the authorized user may select icon orinicator 1196 that is associated with Lucas Bridges, the specific playertraining opportunity screen or player report 3200 is displayed for theselected training opportunity is displayed in FIG. 50. Referring to FIG.50, the player training opportunity screen or player report 3200 isderived from the physiological data that was collected using at leastone of the eight algorithms (e.g., 504, 506, 508, 510, 512, 514, 516,and 518) discussed above. The specific player report displays: (i) thereport time period 3220, (ii) player's name 3222, (iii) player details3224 (e.g., player's number and player's position), (iv) trainingopportunities 3226 triggered by a player during the report time period,(v) the number of alerts 3228 triggered by a player during the reporttime period, (vi) the number of impacts 3230 experienced by a playerduring the report time period, (vii) graphical representation of theimpact locations 3232 the player experienced during the report timeperiod, wherein the percentages of impacts are shown below the graphicalrepresentations of the headform, (viii) alert date 3236, (ix) alert type3238, (x) alert location 3240, (xi) reporting view 3250 of the impactchart 1150, and (xii) training opportunity date 3242. The specificplayer report for Lucas Bridges, FIG. 50, shows that the specific playertriggered one intensity-based training opportunity 1196 on Nov. 16,2017. This training opportunity was based upon the specific playerexperiencing more impacts with high HITsp compared to the player'sprevious history using algorithm 508 and is training opportunity #3.

Alternatively, the authorized user may select icon or indictor 1199 thatis associated with VanderBerg Austin, the specific player trainingopportunity screen or player report 3300 is displayed for the selectedtraining opportunity is displayed in FIG. 51. Referring to FIG. 51, thespecific player training opportunity screen or player report 3300 isderived from the physiological data that was collected using at leastone of the eight algorithms (e.g., 504, 506, 508, 510, 512, 514, 516,and 518) discussed above. The specific player report displays: (i) thereport time period 3320, (ii) player's name 3322, (iii) player details3324 (e.g., player's number and player's position), (iv) trainingopportunities 3326 triggered by a player during the report time period,(v) the number of alerts 3328 triggered by a player during the reporttime period, (vi) the number of impacts 3330 experienced by a playerduring the report time period, (vii) graphical representation of theimpact locations 3332 the specific player experienced by a player duringthe report time period, wherein the percentages of impacts are shownbelow the graphical representations of the headform, (viii) alert date3336, (ix) alert type 3338, (x) alert location 3340, (xi) reporting view3350 of the impact chart 1150, and (xii) training opportunity date 3342.The specific player report for VanderBerg Austin, FIG. 51, shows thatthe specific player triggered three loads based training opportunity1199 on Sep. 9, 2017, Sep. 12, 2017, and Sep. 13, 2017. This trainingopportunity is based upon the specific player experiencing an impactload that is greater than the player's history. This trainingopportunity was determined using algorithm 516 and is trainingopportunity #7.

Further, the authorized user may select icon or indicator 1198 that isassociated with Rex Bruce, the specific player training opportunityscreen or player report 3400 is displayed for the selected trainingopportunity is displayed in FIG. 52. Referring to FIG. 52, the specificplayer training opportunity screen or player report 3200 is derived fromthe physiological data that was collected using at least one of theeight algorithms (e.g., 504, 506, 508, 510, 512, 514, 516, and 518)discussed above. The specific player report displays: (i) the reporttime period 3420, (ii) player's name 3422, (iii) player details 3424(e.g., player's number and player's position), (iv) trainingopportunities 3426 triggered by a player during the report time period,(v) the number of alerts 3428 triggered by a player during the reporttime period, (vi) the number of impacts 3430 experienced by a playerduring the report time period, (vii) graphical representation of theimpact locations 3432 the player experienced by a player during thereport time period, wherein the percentages of impacts are shown belowthe graphical representations of the headform, (viii) alert date 3436,(ix) alert type 3438, (x) alert location 3440, (xi) reporting view 3450of the impact chart 1150, and (xii) training opportunity date 3442. Theplayer report for Rex Bruce, FIG. 52, shows that the specific playertriggered one volume and one load based training opportunities 1198 onSep. 12, 2017. These training opportunities are based upon the specificplayer experiencing an impact load that is greater than the player'shistory and experiencing a higher number of impacts compared to theplayer's history. These training opportunities were determined usingalgorithm 516 and is training opportunity #7 and using algorithm 512 andis training opportunity #5. While only a few player reports that containtraining opportunities were shown in FIGS. 35 and 50-53, it should beunderstood that each of the 19 training opportunities that werediscussed above in detail in connection with FIGS. 14-18 along with theother training opportunities that were discussed more generally inconnection with FIGS. 13B-13D may also be displayed within playerreports. These additional player reports may take a form that is similarto the forms shown in FIGS. 35 and 50-53.

In addition to generating player reports, as shown in FIGS. 35 and50-53, system 10 may also generate other reports. These reports may betime-based reports (e.g., daily, weekly, monthly, seasonally, or custom)or the reports may be player group-based reports (e.g., team, unit,position, or custom). As shown in FIG. 54, these reports may begenerated by manually going into the system 10 using the remote terminal28 and selecting the reports tab 1012. Alternatively, as shown in FIGS.56 and 57, the reports may be automatically generated by the system 10and sent (e.g., via email or text) to the authorized users. Further, thesystem 10 may allow a user to design a custom report (e.g., select thelayout and the information contained within the report) within thesystem 10 and have this custom report automatically generated after apredefined time period, such as a day, week, or month.

Referring to FIG. 54, the authorized user may create the report 3600 forthe entire team over the reporting period. This report 3600 may contain:(i) the HIE load of the team over the reporting period 3610, (ii) theHITsp for the team over the reporting period 3620, (iii) the HIElocation for the team over the reporting period 3630, (iv) a weeklysummary 2640, (v) all alerts that occurred during the reporting period3650, (vi) training opportunities that were triggered over the reportingperiod 3660, (vii) which players are experiencing impacts that aregreater than 95% of their team 3670, and/or (viii) other similarinformation. It should be understood that all or a subset of thisinformation may be included within this report 3600.

FIGS. 55A-55B show zoomed in sections of the report 3600 shown in FIG.54. Specifically, the HIE load of the team over the reporting period3610 shows the number of impacts per day and the severity (e.g., low,medium, and high) of these impacts. The HITsp for the team over thereporting period of 3620 shows the number of alerts that were receivedper day. For example, two single impact alerts were received on Nov. 7,2017. The HIE location for the team over the reporting period 3630breaks down the team into individual positions and then displays thepercentage of impacts per location. For example, an average of allplayers who played offensive line during the reporting period received92% of their impacts in the front of their helmet. In contrast, anaverage of all players who played defensive back during the reportingperiod received 46% of their impacts in the rear of their helmet. Theweekly summary 2640 shows a quick snapshot of the HIE impacts 3641,HITsp impacts that were greater than the 95% for the player's positionand level 3642, HITsp impacts that were greater than the 99% for theplayer's position and level 3643, cumulative alerts 3644, and trainingopportunities 3645 that occurred during the week. Finally, the alerts3650, training opportunities 3660 and 95% impact 3670 provide additionalinformation about which players experienced these impacts.

FIGS. 56 and 57 show reports that can automatically be generated by thesystem 10. These reports provide a significant improvement in theefficiency of the system 10 because it does not have to perform all ofthese algorithms, which also improves the efficiency of the system 10 asit reduces the amount of data that system 10 must simultaneously monitorand process. For example, the algorithms that are described above can beperformed during times when the system 10 is not being used (e.g., lateat night) during practice or game play, which in turn allows the system10 to focus on requests made by authorized users that are activelylogged into the system 10. Additionally, creation of the reports reducethe cost of running the system 10 because the system can tailor itspower usage (e.g., when it performs the above described algorithms) toselect times when power is less expensive and by providing the reportsthe authorized user does not have to log into the system to pull thisinformation therefrom. Further, these reports provides a significantimprovement in the efficiency of using the system 10 by bringingtogether and effectively visually presenting a limited list of highpriority information without requiring the user to navigate throughmultiple screens in order to obtain this information. This in turnimproves the efficiency of using the system 10 because it saves the userfrom even logging into the system 10 let alone navigating throughmultiple screens. These factors tangibly improve the functionality ofthe system 10, particularly the user interface, and more particularlyeffectively displaying the user interface on a remote terminal 28 thathas a small screen (e.g., mobile phone).

The report shown in FIGS. 56 and 57 are similar to the report shown inFIGS. 54-55B. These reports include some similarities and somedifferences. For example, some of the similarities include the fact thatboth include HIE load chart 3740, and HIE location for the team over thereporting period 3750. Some differences between the report shown inFIGS. 54-55B and the report are shown in FIG. 56 include: the comparisonbetween weeks 3710, the total number of impacts broken down by group3720, top contributors 3730, and a weekly comparison of the impactsbased on severity 3760. Instead of a weekly report that is shown in FIG.56, a daily report 3800 that is shown in FIG. 57 could be generated. Thedaily report 3800 contains information that is similar to the weeklyreport 3700, such as the total number of impacts broken down by group3820, top contributors 3830, HIE load chart 3840, and HIE location forthe team over the reporting period 3850. Additionally, the daily reportincludes comparisons between days 3812 and other general information. Itshould be understood that these reports are only example reports and arenot intended to be limiting. As such, it should be understood that areport may include any information that is contained within theteam/national database 32, 38 that the authorized user is allowed toaccess.

FIG. 58 show the roster page 3900 that displays the players 3910, units3920, positions 3930, the IHU 22 identifier 3940, and the helmet model3950. As described above, this page 3900 is the page the authorized userutilizes to enter the player's information and associate thatinformation with a specific IHU 22. Once this occurs, then the IHU 22 istailored to the player by programing in the values/ranges into the IHU22 for the specific player. Also, as discussed above, these originalvalues/ranges may be adjusted or replaced after a predefined number ofimpacts have been experienced. Nevertheless, this roster page 3900provides an overview of the player's and their associated information.Additionally, as discussed above, this roster page 3900 may includeadditional information about each player, such as equipment assignmentsand profiles (e.g., relevant sizes, type of shoes, type of helmet, typeof helmet padding, type of chin strap, type of faceguard, and etc.),medical data for each player (e.g., medical history, injuries, height,weight, emergency information, and etc.), other statistics for theplayers, (viii) workout regiments for the players, other player data(e.g., head and helmet scans, contact information, or class year inschool) and/or etc.

FIG. 59 shows general administrator capabilities that can be accessed byselecting tab 1016 from any screen. These general administratorcapabilities include knowledge about who are authorized users 4010,their email address 4020, their access level 4030, and their status4040. As discussed above, an authorized user may have different accesslevels. For example, Coach MER has super administrator access, whichallows him to access his team data and make any changes therein.However, Coach MER cannot access any other team data or any informationthat is not associated with his team. In contrast, Riddell Demo hasInSite Administrator access, which allows him to access all team datafrom around the country/world. Additionally, contained within theadministrator tab 1016, are general team statistics. Examples of suchteam statistics are shown in FIGS. 60 and 61C. Specifically, FIG. 60show the number of users 4110, user logins 4115, number of players thatare on the roster 4120, number of IHU 22 that are assigned to players4125, number of IHUs 22 that are not assigned to players 4130, firmwareversions of each IHU 4140, information about the alert devices 4150,information about the 25 most recent sessions 4160, and total number ofimpacts 4170. Each of these items can be expanded by selection of thearrow 4105 to display additional information, examples of such are shownin FIGS. 61A-61C.

7. Industrial Application

As is known in the data processing and communications arts, ageneral-purpose computer typically comprises a central processor orother processing device, an internal communication bus, various types ofmemory or storage media (RAM, ROM, EEPROM, cache memory, disk drivesetc.) for code and data storage, and one or more network interface cardsor ports for communication purposes. The software functionalitiesinvolve programming, including executable code as well as associatedstored data. The software code is executable by the general-purposecomputer. In operation, the code is stored within the general-purposecomputer platform. At other times, however, the software may be storedat other locations and/or transported for loading into the appropriategeneral-purpose computer system.

A server, for example, includes a data communication interface forpacket data communication. The server also includes a central processingunit (CPU), in the form of one or more processors, for executing programinstructions. The server platform typically includes an internalcommunication bus, program storage and data storage for various datafiles to be processed and/or communicated by the server, although theserver often receives programming and data via network communications.The hardware elements, operating systems and programming languages ofsuch servers are conventional in nature, and it is presumed that thoseskilled in the art are adequately familiar therewith. The serverfunctions may be implemented in a distributed fashion on a number ofsimilar platforms, to distribute the processing load.

Hence, aspects of the disclosed methods and systems outlined above maybe embodied in programming Program aspects of the technology may bethought of as “products” or “articles of manufacture” typically in theform of executable code and/or associated data that is carried on orembodied in a type of machine-readable medium. “Storage” type mediainclude any or all of the tangible memory of the computers, processorsor the like, or associated modules thereof, such as varioussemiconductor memories, tape drives, disk drives and the like, which mayprovide non-transitory storage at any time for the software programming.All or portions of the software may at times be communicated through theInternet or various other telecommunication networks. Thus, another typeof media that may bear the software elements includes optical,electrical and electromagnetic waves, such as used across physicalinterfaces between local devices, through wired and optical landlinenetworks and over various air-links. The physical elements that carrysuch waves, such as wired or wireless links, optical links or the like,also may be considered as media bearing the software. As used herein,unless restricted to non-transitory, tangible “storage” media, termssuch as computer or machine “readable medium” refer to any medium thatparticipates in providing instructions to a processor for execution.

It is to be understood that the invention is not limited to the exactdetails of construction, operation, exact materials or embodiments shownand described, as obvious modifications and equivalents will be apparentto one skilled in the art. Accordingly, the invention is therefore to belimited only by the scope of the appended claims. While the specificembodiments have been illustrated and described, numerous modificationscome to mind without significantly departing from the spirit of theinvention, and the scope of protection is only limited by the scope ofthe accompanying Claims.

1. A multi-functional system that gathers, analyzes and recordsphysiological parameter data for a plurality of players engaged in playof a contact sport, and provides training opportunities based onanalysis of the recorded physiological parameter data, themulti-functional system comprising: a monitoring unit positioned withina piece of protective sports equipment worn by each of the players, eachmonitoring unit having (i) at least one sensor configured to gather aphysiological parameter data experienced by the player while engaged inplaying the contact, and (ii) a control unit that analyzes and recordsthe physiological parameter data experienced by the player; a databasethat stores the analyzed and recorded physiological parameter data ofthe players that is provided by the monitoring units; a remote terminalhaving a graphical user interface (GUI) configured to (i) receive anddisplay said analyzed and recorded physiological parameter data for theplayers, and (ii) selectively display a training opportunity indicatorto an authorized user when said analyzed and recorded physiologicalparameter data for a specific player exceeds a predetermined thresholdof a previously recorded collection of physiological parameter data. 2.The multi-functional system of claim 1, wherein the previously recordedcollection of physiological parameter data is any one of the following:(i) the specific player's own historical data, and (ii) a team'shistorical data.
 3. The multi-functional system of claim 1, wherein (i)the previously recorded collection of physiological parameter dataincludes a number of alertable impacts other similarly situated playershave received over an alertable time period, and (ii) the specificplayer's physiological parameter data includes a number of alertableimpacts the specific player has received over the alertable time period.4. The multi-functional system of claim 3, wherein other similarlysituated players have at least one characteristic in common with thespecific player, wherein said characteristic is one of either a playinglevel or a playing position.
 5. The multi-functional system of claim 3,wherein the alertable impacts for either of the similarly situatedplayers or the specific player occur when a weighted impact magnitudevalue exceeds a single impact alert threshold, said weighted impactmagnitude value being derived from said physiological parameter data. 6.The multi-functional system of claim 3, wherein the number of alertableimpacts other similarly situated players have received over thealertable time period is automatically updated by the system after apredefined amount of time.
 7. The multi-functional system of claim 1,wherein (i) the previously recorded collection of physiologicalparameter data includes a number of high magnitude impacts othersimilarly situated players have received over an high magnitude timeperiod, and (ii) the specific player's physiological parameter dataincludes a number of high magnitude impacts the specific player hasreceived over the high magnitude time period.
 8. The multi-functionalsystem of claim 7, wherein high magnitude impacts are generated when aweighted impact magnitude value exceeds a high magnitude impactthreshold programmed into the system, said weighted impact magnitudevalue is derived from said physiological parameter data.
 9. Themulti-functional system of claim 1, wherein (i) the previously recordedcollection of physiological parameter data includes a number of impactsother similarly situated players have received over an impact timeperiod, and (ii) the specific player's physiological parameter dataincludes a number of impacts the specific player has received over theimpact time period.
 10. The multi-functional system of claim 1, wherein(i) the previously recorded collection of physiological parameter dataincludes an impact load other similarly situated players have receivedover an impact load time period, and (ii) the specific player'sphysiological parameter data includes an impact load the specific playerhas received over the impact load time period.
 11. The multi-functionalsystem of claim 10, wherein the physiological parameter data is pressureexerted upon a player's head due to an impact, wherein an impactmagnitude value is derived from the physiological parameter data,wherein said impact load is calculated by summing a weighted number ofimpact magnitude values that are calculated by splitting the impactmagnitude values into groups and weighting each group by a severityvalue.
 12. The multi-functional system of claim 1, wherein (i) thepreviously recorded collection of physiological parameter data includesan average historical number of high magnitude impacts the specificplayer has experienced and (ii) the specific player's physiologicalparameter data includes an average recent number of high magnitudeimpacts the specific player has experienced.
 13. The multi-functionalsystem of claim 12, wherein the high magnitude impacts are designated bythe system when a weighted impact magnitude value exceeds a highmagnitude impact threshold, said weighted impact magnitude value beingderived from said physiological parameter data of the specific player.14. The multi-functional system of claim 1, wherein (i) the previouslyrecorded collection of physiological parameter data includes an averagehistorical number of impacts the specific player has experienced and(ii) the specific player's physiological parameter data includes anaverage recent number of impacts the specific player has experienced.15. The multi-functional system of claim 1, wherein an individualmonitoring unit is associated with a player by programming into themonitoring unit at least one threshold that is based on that player'splaying level and position.
 16. The multi-functional system of claim 15,wherein the least one threshold programmed into the monitoring unit isautomatically updated by the system after either: (i) a predefinedamount of time or (ii) the player has received a number of impacts thatexceed a predefined amount of impacts.
 17. The multi-functional systemof claim 1, wherein said monitoring unit includes: (i) a plurality ofsensors arranged in the piece of protective sports equipment wornagainst a player's body part, the sensors configured to detect pressureexerted on the body part resulting from an impact received by the bodypart, (ii) the control unit configured to generate impact data fromimpact magnitude values derived from the pressure detected by thesensors, and (iii) a telemetry element.
 18. The multi-functional systemof claim 17, wherein the control unit is further configured to generatealert data if the weighted impact magnitude value exceeds a singleimpact alert threshold.
 19. The multi-functional system of claim 18,wherein the alert data is transmitted from the monitoring unit to atransmitter within a remote handheld signaling device that is configuredto display the alert data.
 20. A method of gathering, analyzing andrecording physiological parameter data for a plurality of playersengaged in play of a contact sport, and selectively providing trainingopportunities to at least one specific player from amongst the pluralityof players based on further analysis of the recorded physiologicalparameter data, the method comprising: providing (i) a monitoring unitpositioned within a piece of protective sports equipment worn by each ofthe players, the monitoring unit having at least one sensor and acontrol unit, (ii) a database; (iii) a remote terminal having agraphical user interface (GUI); using the at least one sensor to gathera physiological parameter data experienced by each of the players whileengaged in playing the contact sport; analyzing and recording thephysiological parameter data experienced by the players with the controlunit; storing the analyzed and recorded physiological parameter data ofthe players on the database; receiving and displaying the analyzed andrecorded physiological parameter data of the players on the GUI; and,selectively displaying a training opportunity indicator on the GUI to anauthorized user when said analyzed and recorded physiological parameterdata for a specific player exceeds a predetermined threshold of apreviously recorded collection of physiological parameter data that isstored on the database.